O objetivo deste trabalho foi descrever as associações entre os percentuais de gravidez na adolescência e indicadores socioeconômicos e de responsabilidade social dos municípios do estado de Minas Gerais, sudeste do Brasil, no ano de 2000 a 2010. Trata-se de um estudo ecológico, utilizando dados do Sistema de Informações sobre Nascidos Vivos (SINASC). O percentual de nascidos vivos de mães adolescentes para cada município foi calculado segundo o quociente entre o número de nascidos vivos de mães com idade entre 10 e 19 anos e o número total de nascidos vivos registrados no ano de 2000 a 2010. Modelos bayesianos e modelos aditivos generalizados foram utilizados para a obtenção de percentuais de gravidez adolescente ajustados por efeitos espaciais e para avaliar as possíveis associações com os indicadores socioeconômicos e de responsabilidade social. Os percentuais brutos de gravidez adolescente em relação ao total de nascidos vivos nos municípios de Minas Gerais no ano de 2010 variaram de 0 a 46,4%, com uma mediana de 19,6%. O primeiro e o terceiro quartis são, respectivemente, 15,6% e 23,1%. O estudo evidenciou uma estreita relação entre a gravidez na adolescência e indicadores econômicos e sociais. Os percentuais de gravidez adolescente se mostraram maiores nos municípios com menor tamanho populacional, menores valores do Índice de Desenvolvimento Humano e menores valores de outros indicadores de desenvolvimento. A forte relação entre os percentuais de gravidez adolescente e os indicadores sociais e econômicos sugerem que a gravidez adolescente é muito mais um problema social que biológico. Os programas e as ações devem ir muito além de educação sexual e informações sobre métodos preventivos de saúde. / The objective of this study was to describe associations between pregnancy rates in adolescence and socio-economic and social responsibility indicators in the municipalities of the State of Minas Gerais, Southeast of Brazil, in the year of 2010- 2010. This is an ecological study using data from the Brazilian Live Birth Information System (SINASC). The percentage of live births to adolescent mothers for each municipality was calculated based on the quotient between number of born alive infants of mothers aged 10-19 years old and total number of live births in the year of 2000-2010. Bayesian models and generalized additive model were used to obtain the percentages of adolescence pregnancy adjusted for spatial effects and to assess possible associations with socio-economic and social responsibility indicators. The crude percentage of adolescence pregnancy for the total number of live births in the municipalities of Minas Gerais in 2010 ranged from 0 to 46.4%, with median percentage being 19.6% and the first and third quartiles being 15.6% and 23.1%, respectively. This study has demonstrated a close relationship between adolescent pregnancy and socio-economic indicators. Live births to adolescent mothers percentages were found to be higher in municipalities with low population density, low human development index, and other low development indicators. The strong relationship between adolescence pregnancy percentages and socio-economic indicators suggests that adolescent pregnancy is more a social than a biological problem. Therefore, programs and actions should go beyond sexual education and information on preventive health methods.

There is a growing need for accurate distribution data for both common and rare plant species for conservation planning and ecological research purposes. A database of more than 500 observations for nine tree species with different ecological and geographical distributions and a range of frequencies of occurrence in south-eastern New South Wales (Australia) was used to compare the predictive performance of logistic regression models, generalised additive models (GAMs) and classification tree models (CTMs) using different data restriction regimes and several model-building strategies. Environmental variables (mean annual rainfall, mean summer rainfall, mean winter rainfall, mean annual temperature, mean maximum summer temperature, mean minimum winter temperature, mean daily radiation, mean daily summer radiation, mean daily June radiation, lithology and topography) were used to model the distribution of each of the plant species in the study area. Model predictive performance was measured as the area under the curve of a receiver operating characteristic (ROC) plot. The initial predictive performance of logistic regression models and generalised additive models (GAMs) using unrestricted, temperature restricted, major gradient restricted and climatic domain restricted data gave results that were contrary to current practice in species distribution modelling. Although climatic domain restriction has been used in other studies, it was found to produce models that had the lowest predictive performance. The performance of domain restricted models was significantly (p = 0.007) inferior to the performance of major gradient restricted models when the predictions of the models were confined to the climatic domain of the species. Furthermore, the effect of data restriction on model predictive performance was found to depend on the species as shown by a significant interaction between species and data restriction treatment (p = 0.013). As found in other studies however, the predictive performance of GAM was significantly (p = 0.003) better than that of logistic regression. The superiority of GAM over logistic regression was unaffected by different data restriction regimes and was not significantly different within species. The logistic regression models used in the initial performance comparisons were based on models developed using the forward selection procedure in a rigorous-fitting model-building framework that was designed to produce parsimonious models. The rigorous-fitting modelbuilding framework involved testing for the significant reduction in model deviance (p = 0.05) and significance of the parameter estimates (p = 0.05). The size of the parameter estimates and their standard errors were inspected because large estimates and/or standard errors are an indication of model degradation from overfilling or effecls such as mullicollinearily. For additional variables to be included in a model, they had to contribule significantly (p = 0.025) to the model prediclive performance. An attempt to improve the performance of species distribution models using logistic regression models in a rigorousfitting model-building framework, the backward elimination procedure was employed for model selection, bul it yielded models with reduced performance. A liberal-filling model-building framework that used significant model deviance reduction at p = 0.05 (low significance models) and 0.00001 (high significance models) levels as the major criterion for variable selection was employed for the development of logistic regression models using the forward selection and backward elimination procedures. Liberal filling yielded models that had a significantly greater predictive performance than the rigorous-fitting logistic regression models (p = 0.0006). The predictive performance of the former models was comparable to that of GAM and classification tree models (CTMs). The low significance liberal-filling models had a much larger number of variables than the high significance liberal-fitting models, but with no significant increase in predictive performance. To develop liberal-filling CTMs, the tree shrinking program in S-PLUS was used to produce a number of trees of differenl sizes (subtrees) by optimally reducing the size of a full CTM for a given species. The 10-fold cross-validated model deviance for the subtrees was plotted against the size of the subtree as a means of selecting an appropriate tree size. In contrast to liberal-fitting logistic regression, liberal-fitting CTMs had poor predictive performance. Species geographical range and species prevalence within the study area were used to categorise the tree species into different distributional forms. These were then used, to compare the effect of plant species rarity on the predictive performance of logistic regression models, GAMs and CTMs. The distributional forms included restricted and rare (RR) species (Eucalyptus paliformis and Eucalyptus kybeanensis), restricted and common (RC) species (Eucalyptus delegatensis, Eucryphia moorei and Eucalyptus fraxinoides), widespread and rare (WR) species (Eucalyptus data) and widespread and common (WC) species (Eucalyptus sieberi, Eucalyptus pauciflora and Eucalyptus fastigata). There were significant differences (p = 0.076) in predictive performance among the distributional forms for the logistic regression and GAM. The predictive performance for the WR distributional form was significantly lower than the performance for the other plant species distributional forms. The predictive performance for the RC and RR distributional forms was significantly greater than the performance for the WC distributional form. The trend in model predictive performance among plant species distributional forms was similar for CTMs except that the CTMs had poor predictive performance for the RR distributional form. This study shows the importance of data restriction to model predictive performance with major gradient data restriction being recommended for consistently high performance. Given the appropriate model selection strategy, logistic regression, GAM and CTM have similar predictive performance. Logistic regression requires a high significance liberal-fitting strategy to both maximise its predictive performance and to select a relatively small model that could be useful for framing future ecological hypotheses about the distribution of individual plant species. The results for the modelling of plant species for conservation purposes were encouraging since logistic regression and GAM performed well for the restricted and rare species, which are usually of greater conservation concern.

Made available in DSpace on 2015-05-08T14:53:27Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1842165 bytes, checksum: 59f17268b1e23252f9bc9e0e37474638 (MD5) Previous issue date: 2012-12-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / In recent years, several studies have been published dealing on factors that influence consumer purchase intent in various economic sectors. In this line of work, we are specifically for the sector of collective buying, obtain regression models that could contribute to the study of the relationship between the purchase intention and the characteristics of market segments. The aim is to assist in the inclusion of this variable purchase intention in the process of choosing a target group, guiding decisions to meet more effectively service consumers. To achieve this goal, initially interviewed 384 Internet users in the city of João Pessoa, Paraíba, Brazil. Then the data obtained from interviews, were used to estimate those models. These models were based on assumptions of theories of the cognitive approach of consumer behavior, especially the Theory of Reasoned Action. The instrument used for data collection was a questionnaire containing market research questions related to psychological factors, socio-cultural and situational consumer. The most successful model was a generalized additive model with nine variables and nonparametric one end, obtained from smoothing splines. This model had a pseudo-R2 of 0,89 and allowed to reach a percentage of correct trials of the observations of the sample equal to 94%. With the aid of simulations, it was observed how the proposed model type is capable of assisting in selection of a target with a higher interest in the use of the service. It was also shown how the model can be used to evaluate production systems, in relation to more efficient service to customers intend to use the service. The generalized additive models were effective for identifying the presence of nonlinear relationships and were able to generate a high explanatory power of the propensity of individuals to use specific service. / Nos últimos anos, vários estudos foram publicados versando sobre fatores que influenciam a intenção de compra do consumidor em diversos setores econômicos. Nesta linha de trabalho, procurou-se, especificamente para o setor de compra coletivas, obter modelos de regressão que pudessem contribuir para o estudo da relação entre a intenção de compra e as características de segmentos de mercado. Visa-se com isso auxiliar na inclusão da variável intenção de compras no processo de escolha de um público-alvo, orientando decisões para satisfazer com maior eficiência consumidores do serviço. Para alcançar o objetivo, entrevistaram-se inicialmente 384 usuários de Internet da cidade de João Pessoa, Paraíba, Brasil. Em seguida os dados obtidos a partir de entrevistas, foram usados para estimar aqueles modelos. Esses modelos foram baseados em pressupostos de teorias da abordagem cognitiva do comportamento do consumidor, especialmente da Teoria da Ação Racional. O instrumento usado para a coleta de dados foi um questionário de pesquisa de mercado contendo questões ligadas a fatores psicológicos, sócio-culturais e situacionais do consumidor. O modelo mais bem sucedido foi um modelo aditivo generalizado com nove variáveis e com um termo não paramétrico, obtido a partir do método de suavização splines. Esse modelo apresentou um pseudo-R2 igual a 0,89 e possibilitou alcançar um percentual de acertos nos julgamentos das observações da amostra igual a 94%. Com o auxílio de simulações, verificou-se de que modo o tipo de modelo proposto é capaz de auxiliar na escolha de um público-alvo com maior interesse no uso do serviço. Apresentou-se ainda a maneira pela qual o modelo pode ser usado para avaliar sistemas produtivos, em relação ao atendimento mais eficiente de clientes que têm a intenção de utilizar o serviço. Os modelos aditivos generalizados mostraram-se eficientes para identificar a presença de relações não lineares e foram capazes de gerar um poder explicativo alto da propensão de indivíduos para utilizar um serviço específico.

Orientador: Ronaldo Dias / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Matemática Estatística e Computação Científica / Made available in DSpace on 2018-08-25T00:25:46Z (GMT). No. of bitstreams: 1 Morellato_SauloAlmeida_D.pdf: 32854783 bytes, checksum: 040664acd0c8f1efe07cedccda8d11f6 (MD5) Previous issue date: 2014 / Resumo: Este trabalho aborda dois problemas de inferência relacionados à regressão múltipla não paramétrica: a estimação em modelos aditivos usando um método não paramétrico e o teste de hipóteses para igualdade de curvas ajustadas a partir do modelo. Na etapa de estimação é construída uma generalização dos métodos h-splines, tanto no contexto sequencial adaptativo proposto por Dias (1999), quanto no contexto bayesiano proposto por Dias e Gamerman (2002). Os métodos h-splines fornecem uma escolha automática do número de bases utilizada na estimação do modelo. Estudos de simulação mostram que os resultados obtidos pelos métodos de estimação propostos são superiores aos conseguidos nos pacotes gamlss, mgcv e DPpackage em R. São criados dois testes de hipóteses para testar H0 : f = f0. Um teste de hipóteses que tem sua regra de decisão baseada na distância quadrática integrada entre duas curvas, referente à abordagem sequencial adaptativa, e outro baseado na medida de evidência bayesiana proposta por Pereira e Stern (1999). No teste de hipóteses bayesiano o desempenho da medida de evidência é observado em vários cenários de simulação. A medida proposta apresentou um comportamento que condiz com uma medida de evidência favorável à hipótese H0. No teste baseado na distância entre curvas, o poder do teste foi estimado em diversos cenários usando simulações e os resultados são satisfatórios. Os procedimentos propostos de estimação e teste de hipóteses são aplicados a um conjunto de dados referente ao trabalho de Tanaka e Nishii (2009) sobre o desmatamento no leste da Ásia. O objetivo é escolher um entre oito modelos candidatos. Os testes concordaram apontando um par de modelos como sendo os mais adequados / Abstract: In this work we discuss two inference problems related to multiple nonparametric regression: estimation in additive models using a nonparametric method and hypotheses testing for equality of curves, also considering additive models. In the estimation step, it is constructed a generalization of the h-splines method, both in the sequential adaptive context proposed by Dias (1999), and in the Bayesian context proposed by Dias and Gamerman (2002). The h-splines methods provide an automatic choice of the number of bases used in the estimation of the model. Simulation studies show that the results obtained by proposed estimation methods are superior to those achieved in the packages gamlss, mgcv and DPpackage in R. Two hypotheses testing are created to test H0 : f = f0. A hypotheses test that has a decision rule based on the integrated squared distance between two curves, for adaptive sequential approach, and another based on the Bayesian evidence measure proposed by Pereira and Stern (1999). In Bayesian hypothesis testing the performance measure of evidence is observed in several simulation scenarios. The proposed measure showed a behavior that is consistent with evidence favorable to H0. In the test based on the distance between the curves, the power of the test was estimated at various scenarios using simulations, and the results are satisfactory. At the end of the work the proposed procedures of estimation and hypotheses testing are applied in a dataset concerning to the work of Tanaka and Nishii (2009) about the deforestation in East Asia. The objective is to choose one amongst eight models. The tests point to a pair of models as being the most suitableIn this work we discuss two inference problems related to multiple nonparametric regression: estimation in additive models using a nonparametric method and hypotheses testing for equality of curves, also considering additive models. In the estimation step, it is constructed a generalization of the h-splines method, both in the sequential adaptive context proposed by Dias (1999), and in the Bayesian context proposed by Dias and Gamerman (2002). The h-splines methods provide an automatic choice of the number of bases used in the estimation of the model. Simulation studies show that the results obtained by proposed estimation methods are superior to those achieved in the packages gamlss, mgcv and DPpackage in R. Two hypotheses testing are created to test H0 : f = f0. A hypotheses test that has a decision rule based on the integrated squared distance between two curves, for adaptive sequential approach, and another based on the Bayesian evidence measure proposed by Pereira and Stern (1999). In Bayesian hypothesis testing the performance measure of evidence is observed in several simulation scenarios. The proposed measure showed a behavior that is consistent with evidence favorable to H0. In the test based on the distance between the curves, the power of the test was estimated at various scenarios using simulations, and the results are satisfactory. At the end of the work the proposed procedures of estimation and hypotheses testing are applied in a dataset concerning to the work of Tanaka and Nishii (2009) about the deforestation in East Asia. The objective is to choose one amongst eight models. The tests point to a pair of models as being the most suitable / Doutorado / Estatistica / Doutor em Estatística

This study focuses on the habitats of cetaceans in the Mid-Atlantic Bight, a region characterized by bathymetric diversity and the presence of distinct water masses (i.e. the shelf water, slope water, and Gulf Stream). The combination of these features contributes to the hydrographic complexity of the area, which furthermore influences biological productivity and potential prey available for cetaceans. The collection of cetacean sighting data together with physical oceanographic data can be used to examine cetacean habitat associations. Cetacean habitat modeling is a mechanism for predicting cetacean distribution patterns based on environmental variables such as bathymetric and physical properties, and for exploring the potential ecological implications that contribute to cetacean spatial distributions. We can advance conservation efforts of cetacean populations by expanding our knowledge of their habitats and distribution. Generalized additive models (GAMs) were developed to predict the spatial distribution patterns of sperm whales (Physeter macrocephalus), pilot whales (Globicephala spp.), bottlenose dolphins (Tursiops truncatus), and Atlantic spotted dolphins (Stenella frontalis) based on significant physical parameters along the continental shelf-break region in the Mid-Atlantic Bight. Data implemented in the GAMs were collected in the summer of 2006 aboard the NOAA R/V Gordon Gunter. These included visual cetacean survey data collected along with physical data at depth via expendable bathythermograph (XBT), and conductivity-temperature-depth (CTD) instrumentation. Additionally, continual surface data were collected via the ship’s flow through sensor system. Interpolations of physical data were created from collected point data using the inverse distant weighted method (IDW) to estimate the spatial distribution of physical data within the area of interest. Interpolated physical data, as well as bathymetric (bottom depth and slope) data were extracted to overlaid cetacean sightings, so that each sighting had an associated value for nine potentially significant physical habitat parameters. A grid containing 5x5 km grid cells was created over the study area and cetacean sightings along with the values for each associated habitat parameter were summarized in each grid cell. Redundant parameters were reduced, resulting in a full model containing temperature at 50 m depth, mixed layer depth, bottom depth, slope, surface temperature, and surface salinity. GAMs were fit for each species based on these six potentially significant parameters. The resultant fit models for each species predicted the number of individuals per km2 based on a unique combination of environmental parameters. Spatial prediction grids were created based on the significant habitat parameters for each species to illustrate the GAM outputs and to indicate predicted regions of high density. Predictions were consistent with observed sightings. Sperm whale distribution was predicted by a combination of depth, sea surface temperature, and sea surface salinity. The model for pilot whales included bottom slope, and temperature at 50 m depth. It also indicated that mixed layer depth, bottom depth and surface salinity contributed to group size. Similarly, temperature at 50 m depth was significant for Atlantic spotted dolphins. Predicted bottlenose dolphin distribution was determined by a combination of bottom slope, surface salinity, and temperature at 50 m depth, with mixed layer depth contributing to group size. Distribution is most likely a sign of prey availability and ecological implications can be drawn from the habitat parameters associated with each species. For example, regions of high slope can indicate zones of upwelling, enhanced vertical mixing and prey availability throughout the water column. Furthermore, surface temperature and salinity can be indicative of patchy zones of productivity where potential prey aggregations occur. The benefits of these models is that collected point data can be used to expand our knowledge of potential cetacean “hotspots” based on associations with physical parameters. Data collection for abundance estimates, higher resolution studies, and future habitat surveys can be adjusted based on these model predictions. Furthermore, predictive habitat models can be used to establish Marine Protected Areas with boundaries that adapt to dynamic oceanographic features reflecting potential cetacean mobility. This can be valuable for the advancement of cetacean conservation efforts and to limit potential vessel and fisheries interactions with cetaceans, which may pose a threat to the sustainability of cetacean populations.

The Birnbaum-Saunders (BS) distribution is the most popular model used to describe lifetime process under fatigue. Throughout the years, this distribution has received a wide ranging of applications, demanding some more flexible extensions to solve more complex problems. One of the most well-known extensions of the BS distribution is the generalized Birnbaum- Saunders (GBS) family of distributions that includes the Birnbaum-Saunders special-case (BSSC) and the Birnbaum-Saunders generalized t (BSGT) models as special cases. Although the BS-SC distribution was previously developed in the literature, it was never deeply studied and hence, in this thesis, we provide a full Bayesian study and develop a tool to generate random numbers from this distribution. Further, we develop a very flexible regression model, that admits different degrees of skewness and kurtosis, based on the BSGT distribution using the generalized additive models for location, scale and shape (GAMLSS) framework. We also introduce a new extension of the BS distribution called the Birnbaum-Saunders power (BSP) family of distributions, which contains several special or limiting cases already published in the literature, including the GBS family. The main feature of the new family is that it can produce both unimodal and bimodal shapes depending on its parameter values. We also introduce this new family of distributions into the GAMLSS framework, in order to model any or all the parameters of the distribution using parametric linear and/or nonparametric smooth functions of explanatory variables. Throughout this thesis we present five different applications in real data sets in order to illustrate the developed theoretical results. / A distribuição Birnbaum-Saunders (BS) é o modelo mais popular utilizado para descrever processos de fadiga. Ao longo dos anos, essa distribuição vem recebendo aplicações nas mais diversas áreas, demandando assim algumas extensões mais flexíveis para resolver problemas mais complexos. Uma das extensões mais conhecidas na literatura é a família de distribuições Birnbaum-Saunders generalizada (GBS), que inclui as distribuições Birnbaum-Saunders casoespecial (BS-SC) e Birnbaum-Saunders t generalizada (BSGT) como modelos especiais. Embora a distribuição BS-SC tenha sido previamente desenvolvida na literatura, nunca foi estudada mais profundamente e, assim, nesta tese, um estudo bayesiano é desenvolvido acerca da mesma além de um novo gerador de números aleatórios dessa distribuição ser apresentado. Adicionalmente, um modelo de regressão baseado na distribuição BSGT é desenvolvido utilizando-se os modelos aditivos generalizados para locação, escala e forma (GAMLSS), os quais apresentam grande flexibilidade tanto para a assimetria como para a curtose. Uma nova extensão da distribuição BS também é apresentada, denominada família de distribuições Birnbaum-Saunders potência (BSP), que contém inúmeros casos especiais ou limites já publicados na literatura, incluindo a família GBS. A principal característica desta nova família é que ela é capaz de produzir formas tanto uni como bimodais dependendo do valor de seus parâmetros. Esta nova família também é introduzida na estrutura dos modelos GAMLSS para fornecer uma ferramenta capaz de modelar todos os parâmetros da distribuição como funções lineares e/ou não-lineares suavizadas de variáveis explicativas. Ao longo desta tese são apresentadas cinco diferentes aplicações em conjuntos de dados reais para ilustrar os resultados teóricos obtidos.

A percepção e interpretação da interação de indivíduos e populações com o ambiente e a forma como tal relação condiciona sua distribuição espacial é questão-chave e recorrente em estudos ecológicos. Padrões de uso de área observados para populações emergem em ultima análise da variabilidade entre seus indivíduos em selecionar habitats e interagir com os mesmos. Este estudo teve como foco o uso de área pela população do boto-cinza, Sotalia guianensis, e sua variabilidade individual no estuário de Cananeia, localizado na costa sudeste do Brasil (25°03\' S; 47°55\' W), durante o verão e o inverno de 2015 e o verão de 2016. Parâmetros ambientais e geográficos (distâncias da desembocadura de rios, da entrada do estuário e de áreas urbanas, profundidade, maré e autocorrelação espacial) foram testados para explicar a distribuição da população e de seus indivíduos a partir de funções de probabilidade de seleção de recursos (RSPF) em modelos aditivos generalizados (GAM). Onze indivíduos fotoidentificados com 18 ou mais recapturas foram avaliados com o uso de modelos individuais de ocupação e sua interpretação foi subsidiada por estimativas de áreas domiciliares obtidas a partir de kerneis fixos de densidade. Nas três temporadas a população apresentou densidades de grupos desiguais ao longo do estuário e todas as variáveis, com exceção da distância de áreas urbanas, explicaram as probabilidades de presença observadas. Análises individuais revelaram discrepâncias nos tamanhos e disposição geográfica de áreas domiciliares e diferenças na composição e estimativa dos parâmetros selecionados para cada indivíduo. A variabilidade individual na população deve ter papel fundamental em termos de utilização do espaço e seleção de habitat pelo boto-cinza no estuário local. / Understanding and interpreting the interaction of individuals and populations with the environment and how this relationship outlines their spatial distribution is a key question common in ecological studies. Area use patterns observed for populations are ultimately an outcome from individual variability in habitat selection and their interaction with such environments. Are use and habitat selection by the population of Guiana dolphins, Sotalia guianensis, and its individual variability were accessed in the Cananeia estuary (25°03\' S; 47°55\' W), southeastern Brazil, during the summer and winter of 2015 and the summer of 2016. Environmental and geographic parameters were estimated aiming to explain population distribution and differences within individuals. For this purpose, resource selection probability functions (RSPF) were applied in generalized additive models (GAM). Covariates tested included: distance to river mouths, distance to the estuary entrance, distance to urban areas, depth and tide. Geographic coordinates were used to model spatial autocorrelation. Eleven photo-identified individuals had their occupancy modelled and accessed in relation to their home range obtained from fixed kernel densities estimates. The population exhibited patchy group densities throughout the estuary in all seasons. Except from distance to urban areas all variables were selected in our final model for the population\'s RSPF. Individual analysis revealed discrepancies in size and location of home ranges which lead to remarkable differences in the composition and estimates of parameters selected in the models for each individual.

A percepção e interpretação da interação de indivíduos e populações com o ambiente e a forma como tal relação condiciona sua distribuição espacial é questão-chave e recorrente em estudos ecológicos. Padrões de uso de área observados para populações emergem em ultima análise da variabilidade entre seus indivíduos em selecionar habitats e interagir com os mesmos. Este estudo teve como foco o uso de área pela população do boto-cinza, Sotalia guianensis, e sua variabilidade individual no estuário de Cananeia, localizado na costa sudeste do Brasil (25°03\' S; 47°55\' W), durante o verão e o inverno de 2015 e o verão de 2016. Parâmetros ambientais e geográficos (distâncias da desembocadura de rios, da entrada do estuário e de áreas urbanas, profundidade, maré e autocorrelação espacial) foram testados para explicar a distribuição da população e de seus indivíduos a partir de funções de probabilidade de seleção de recursos (RSPF) em modelos aditivos generalizados (GAM). Onze indivíduos fotoidentificados com 18 ou mais recapturas foram avaliados com o uso de modelos individuais de ocupação e sua interpretação foi subsidiada por estimativas de áreas domiciliares obtidas a partir de kerneis fixos de densidade. Nas três temporadas a população apresentou densidades de grupos desiguais ao longo do estuário e todas as variáveis, com exceção da distância de áreas urbanas, explicaram as probabilidades de presença observadas. Análises individuais revelaram discrepâncias nos tamanhos e disposição geográfica de áreas domiciliares e diferenças na composição e estimativa dos parâmetros selecionados para cada indivíduo. A variabilidade individual na população deve ter papel fundamental em termos de utilização do espaço e seleção de habitat pelo boto-cinza no estuário local. / Understanding and interpreting the interaction of individuals and populations with the environment and how this relationship outlines their spatial distribution is a key question common in ecological studies. Area use patterns observed for populations are ultimately an outcome from individual variability in habitat selection and their interaction with such environments. Are use and habitat selection by the population of Guiana dolphins, Sotalia guianensis, and its individual variability were accessed in the Cananeia estuary (25°03\' S; 47°55\' W), southeastern Brazil, during the summer and winter of 2015 and the summer of 2016. Environmental and geographic parameters were estimated aiming to explain population distribution and differences within individuals. For this purpose, resource selection probability functions (RSPF) were applied in generalized additive models (GAM). Covariates tested included: distance to river mouths, distance to the estuary entrance, distance to urban areas, depth and tide. Geographic coordinates were used to model spatial autocorrelation. Eleven photo-identified individuals had their occupancy modelled and accessed in relation to their home range obtained from fixed kernel densities estimates. The population exhibited patchy group densities throughout the estuary in all seasons. Except from distance to urban areas all variables were selected in our final model for the population\'s RSPF. Individual analysis revealed discrepancies in size and location of home ranges which lead to remarkable differences in the composition and estimates of parameters selected in the models for each individual.

Neste trabalho apresentamos os modelos lineares parciais generalizados com uma variável explicativa contínua tratada de forma não paramétrica e os modelos lineares parciais aditivos generalizados com no mínimo duas variáveis explicativas contínuas tratadas de tal forma. São utilizados os P-splines para descrever a relação da variável resposta com as variáveis explicativas contínuas. Sendo assim, as funções de verossimilhança penalizadas, as funções escore penalizadas e as matrizes de informação de Fisher penalizadas são desenvolvidas para a obtenção das estimativas de máxima verossimilhança penalizadas por meio da combinação do algoritmo backfitting (Gauss-Seidel) e do processo iterativo escore de Fisher para os dois tipos de modelo. Em seguida, são apresentados procedimentos para a estimação do parâmetro de suavização, bem como dos graus de liberdade efetivos. Por fim, com o objetivo de ilustração, os modelos propostos são ajustados à conjuntos de dados reais. / In this work we present the generalized partial linear models with one continuous explanatory variable treated nonparametrically and the generalized additive partial linear models with at least two continuous explanatory variables treated in such a way. The P-splines are used to describe the relationship among the response and the continuous explanatory variables. Then, the penalized likelihood functions, penalized score functions and penalized Fisher information matrices are derived to obtain the penalized maximum likelihood estimators by the combination of the backfitting (Gauss-Seidel) algorithm and the Fisher escoring iterative method for the two types of model. In addition, we present ways to estimate the smoothing parameter as well as the effective degrees of freedom. Finally, for the purpose of illustration, the proposed models are fitted to real data sets.

Neste trabalho apresentamos os modelos lineares parciais generalizados com uma variável explicativa contínua tratada de forma não paramétrica e os modelos lineares parciais aditivos generalizados com no mínimo duas variáveis explicativas contínuas tratadas de tal forma. São utilizados os P-splines para descrever a relação da variável resposta com as variáveis explicativas contínuas. Sendo assim, as funções de verossimilhança penalizadas, as funções escore penalizadas e as matrizes de informação de Fisher penalizadas são desenvolvidas para a obtenção das estimativas de máxima verossimilhança penalizadas por meio da combinação do algoritmo backfitting (Gauss-Seidel) e do processo iterativo escore de Fisher para os dois tipos de modelo. Em seguida, são apresentados procedimentos para a estimação do parâmetro de suavização, bem como dos graus de liberdade efetivos. Por fim, com o objetivo de ilustração, os modelos propostos são ajustados à conjuntos de dados reais. / In this work we present the generalized partial linear models with one continuous explanatory variable treated nonparametrically and the generalized additive partial linear models with at least two continuous explanatory variables treated in such a way. The P-splines are used to describe the relationship among the response and the continuous explanatory variables. Then, the penalized likelihood functions, penalized score functions and penalized Fisher information matrices are derived to obtain the penalized maximum likelihood estimators by the combination of the backfitting (Gauss-Seidel) algorithm and the Fisher escoring iterative method for the two types of model. In addition, we present ways to estimate the smoothing parameter as well as the effective degrees of freedom. Finally, for the purpose of illustration, the proposed models are fitted to real data sets.