Yellow perch (Perca flavescens) in Lake Erie support valuable commercial and recreational fisheries critical to the local economy and society. The study of yellow perch's temporal and spatial population dynamics is important for both stock assessment and fisheries management. I explore the spatial and temporal variation of the yellow perch population by analyzing the fishery-independent surveys in Lake Erie. Model-based approaches were developed to estimate the relative abundance index, which reflected the temporal variation of the population. I also used design-based approaches to deal with the situation in which population density varied both spatially and temporally. I first used model-based approaches to explore the spatial and temporal variation of the yellow perch population and to develop the relative abundance index needed. Generalized linear models (GLM), spatial generalized linear models (s-GLM), and generalized additive models (GAM) were compared by examining the goodness-of-fit, reduction of spatial autocorrelation, and prediction errors from cross-validation. The relationship between yellow perch density distribution and spatial and environmental factors was also studied. I found that GAM showed the best goodness-of-fit shown as AIC and lowest prediction errors but s-GLM resulted in the best reduction of spatial autocorrelation. Both performed better than GLM for yellow perch relative abundance index estimation. I then applied design-based approaches to study the spatial and temporal population dynamics of yellow perch through both practical data analysis and simulation. The currently used approach in Lake Erie is stratified random sampling (StRS). Traditional sampling designs (simple random sampling (SRS) and StRS) and adaptive sampling designs (adaptive two-phase sampling (ATS), adaptive cluster sampling (ACS), and adaptive two-stage sequential sampling (ATSS)) for fishery-independent surveys were compared. From accuracy and precision aspect, ATS performed better than the SRS, StRS, ACS and ATSS for yellow perch fishery-independent survey data in Lake Erie. Model-based approaches were further studied by including geostatistical models. The performance of the GLM and GAM models and geostatistical models (spatial interpolation) were compared when they are used to analyze the temporal and spatial variation of the yellow perch population through a simulation study. This is the first time that these two types of model- based approaches have been compared in fisheries. I found that arithmetic mean (AM) method was only preferred when neither environment factors nor spatial information of sampling locations were available. If the survey can not cover the distribution area of the population due to biased design or lack of sampling locations, GLMs and GAMs are preferable to spatial interpolation (SI). Otherwise, SI is a good alternative model to estimate relative abundance index. SI has rarely been realized in fisheries. Different models may be recommended for different species/fisheries when we estimate their spatial-temporal dynamics, and also the most appropriate survey designs may be different for different species. However, the criteria and approaches for the comparison of both model-based and design-based approaches will be applied for different species or fisheries. / Ph. D.

This thesis investigates a newly developed term coined by the author called generalized identification, which is the tendency that people who identify high with one group tend to identify high with other groups as well, and how personality variables from the Five-Factor model may relate to this tendency. A common component of identification towards 10 preselected groups was calculated (N = 148), with a principal component analysis. The result reveal that the generalized identification account for 41 % of the total variance. A stepwise multiple regression analysis further showed that Openness to Experience and Agreeableness from the Five-Factor model explained 26 % of the variance in the generalized identification. However, due to low reliability when measuring personality traits, the relationship between personality and generalized identification could not be interpreted in a satisfying way, and it needs to be further explored before drawing firm conclusions.

We apply anisotropic Banach space methods together with operator renewal theory to obtain polynomial rates of decay of correlations for a class of generalized baker's transformations. The polynomial rates were proved for a smaller class of observables in a 2013 paper of Bose and Murray by fundamentally different methods. Our approach provides a direct analysis of the Frobenius-Perron operator associated to a generalized baker's transformation in contrast to the paper of Bose and Murray where decay rates are obtained for a factor map and lifted to the full map. / Graduate

International Telemetering Conference Proceedings / October 25-28, 1999 / Riviera Hotel and Convention Center, Las Vegas, Nevada / The mathematics basis that can form a telemetry system is orthogonal functions. Three kinds of orthogonal functions are used up to now. First of them is sine and cosine function family. The second one is block pulse function family. The third one is Walsh function family. Their corresponding telemetry systems are FDM, TDM and SDM (CDM). Later we introduced an orthogonal function which is called Bridge function. The corresponding system is named telemetry system based on Bridge function. Now a new kind of orthogonal function, Generalized Bridge function, has been found. It can be applied to practical multiplex of information transmission. In this paper the author provides the design concept, block diagram, operational principle and technical realization of telemetry system based on Generalized Bridge function.

Despite the ostensible prevalence of academic worry at the college and university level, there is a paucity of research in this area. In addition, there is an even greater dearth of research investigating treatments for excessive and uncontrollable academic worry. Further, the research on non-pharmacological treatment strategies for reducing pathological worry (as seen in its most severe form in generalized anxiety disorder; GAD) is limited. The primary goal of this study was to investigate the potential benefits of two self-administered interventions for reducing pathological academic worry. Participants experiencing pathological academic worry (N = 113) were randomized to one of four conditions: (a) worry exposure (WE), (b) expressive writing (EW), (c) a credible placebo control, consisting of pulsed audio-photic stimulation (APS), and (d) wait-list control (WLC). Participants were instructed to practice their interventions three times per week for one month. Participants in all three of the intervention conditions showed significant improvement on self-report measures, while no such changes were observed for the control group. Findings were mixed on the objective measures. In general, neither the WE nor EW conditions consistently outperformed placebo, and in some cases, EW failed to outperform the waitlist control group at post-treatment. Overall, those assigned to WE showed greater improvement than those assigned to EW at posttreatment, but few significant differences between the three intervention groups emerged at follow-up. These mixed findings suggest that either the efficacy of each of the treatments does not go beyond the that which would be expected of non-specific treatment effects, or that the pulsed audio-photic stimulation did in fact exert more of an effect than a typical placebo, suggesting there may have been an unanticipated active treatment component. Despite this, several participants in WE and EW showed marked improvement, and even continued improvement by follow-up, suggesting that, while perhaps not highly potent treatments when delivered in isolation, these may be easy, costeffective interventions for pathological worry. Further research is needed with clinical GAD samples, and research is also needed on the placebo response rate in GAD. / text

Approved for public release; distribution in unlimited. / Some battlefield models have a component in them which models the time it takes for an observer to detect a target. Different observers may have different mean detection times due to various factors such as the type of sensor used, environmental conditions, fatigue of the observer, etc. Two parametric models for the distribution of time to target detection are considered which can incorporate these factors. Maximum likelihood estimation procedures for the parameters are described. Results of simulation experiments to study the small sample behavior of the estimators are presented. / http://archive.org/details/modelsfortargetd00baed / Major, Korean Air Force

Charles University in Prague Faculty of Social Sciences Institute of Economic Studies MASTER THESIS The determinants of access to finance. Evidence for transition economies Author: Alina Cazachevici Supervisor: Roman Horvath, Ph.D. Academic Year: 2012/2013 Abstract The thesis provides an empirical analysis of impact of country-level and firm-level determinants on access to finance in transition economies. Generalized Ordered Logit model is applied on survey data for transition countries, combined with financial market indicators. The results show that higher concentration in banking sector, as well as higher financial deepening have a positive impact on access to finance, while volatile macroeconomic environment, higher implication of foreign-owned and state- owned banks seems to be perceived as increasing obstacles in accessing external financing. Combining indexes for liberalization in banking sector and liberalization of securities markets proved that before liberalization process firms had better access to finance. One of the possible explanations is that before liberalization state banks were forced by politicians to issue more loans, while after reforms the political pressure was removed, imposing stricter conditions for loan granting. Inclusion of corruption variable yields expectable results that...

Displays are ubiquitous throughout the animal kingdom. While many have been thoroughly documented, the factors affecting the expression of such displays are still not fully understood. We tested the hypotheses that display production would be affected by ecological context (i.e. the identity of the receiver) and intrinsic qualities of the signaler (i.e. heavyweight and lightweight size class) in the green anole lizard, Anolis carolinensis. Our results supported these predictions and show that a) ecological context, specifically displaying to conspecifics, has the greatest impact on display production; b) size class influenced display rate with heavyweight males displaying more to green females and lightweight males displaying more to green males in similar frequency between the two size classes to their respective target stimuli. Furthermore, our results provide empirical support for differential use of the three major display types (A, B and C displays), and uncover unexpected complexity in green anole display production.

Diversas situações práticas exigem a análise de series temporais de contagem, que podem apresentar tendência, sazonalidade e efeitos de variáveis explicativas. A motivação do nosso trabalho é a análise de internações diárias por doenças respiratórias para pessoas com mais que 65 anos residentes no município de São Paulo. O efeito de variáveis climáticas e concentrações de poluentes foram incluídos nos modelos e foram usadas as funções seno e cosseno com periodicidade de um ano para explicar o padrão sazonal e obter os efeitos das variáveis climáticas e poluentes controlando essa sazonalidade. Outro aspecto a ser considerado é a inclusão da população nas análises de modo que a interpretação dos efeitos seja para as taxas diárias de internações. Diferentes modelos paramétricos foram propostos para as internações. O mais simples é o modelo de regressão linear para o logaritmo das taxas. Foram ajustados os modelos lineares generalizados (MLG) para as internações com função de ligação logaritmo e com a população como offset, por este modelo permitir o uso das distribuições Poisson e Binomial Negativa, usadas para dados de contagem. Devido à heteroscedasticidade extra, foram propostos modelos GAMLSS incluindo variáveis para explicar o desvio padrão. Foram ajustados modelos ARMA e GARMA, por incluírem uma estrutura de correlação serial. O objetivo desse trabalho é comparar as estimativas, os erros padrões, a cobertura dos intervalos de confiança e o erro quadrático médio para o valor predito segundo os vários modelos e a escolha do modelo mais apropriado, que depende da completa análise de resíduos, geralmente omitida na literatura. O modelo GARMA com distribuição Binomial Negativa apresentou melhor ajuste, pois os erros parecem seguir a distribuição proposta e tem baixa autocorrelação, além de ter tido uma boa cobertura pelo intervalo de confiança e um baixo erro quadrático médio. Também foi analisado o efeito da autocorrelação dos dados nas estimativas nos vários modelos baseado em dados simulados. / Many practical situations require the analysis of time series of counts, which may present trend, seasonality and effects of covariates. The motivation of this work is the analysis of daily hospital admissions for respiratory diseases in people over 65 living in the city of São Paulo. The effect of climatic variables and concentrations of pollutants were included in the models and the sine and cosine functions with annual period were included to explain the seasonal pattern and obtain the effects of pollutants and climatic variables partially controlled by this seasonality. Another aspect to be considered is the inclusion of the population in the analys es in order to interpret the effects based on daily hospitalization rates . Different parametric models have been proposed for hospitalizations. The simplest is the linear regression model for the logarithm of the hospitalization rate. The generalized linear models (GLM) were adjusted for daily admissions with logarithmic link function and the population as offset to consider the Poisson and Negative Binomial distributions for counting data. Due to the extra heteroscedasticity, GAMLSS models were proposed including variables to explain the standard error. Moreover, the ARMA and GARMA models were fitted to include the serial correlation structure. The aim of this work is to compare estimates, standard errors, coverage of confidence intervals and mean squared error of predicted value for the various models and choose the most appropriate model, which depends on a complete analysis of residuals, usually omitted in the literature. The GARMA model with Negative Binomial distribution was the best fit since the errors seem to follow the proposed distribution and they have small values of autocorrelation. Besides, this model had low mean squared error and a good coverage of confidence interval. The effect of autocorrelation of data in the estimates was also analyzed in the setting of several models based on simulated data.

by Kim Hung Lo. / Thesis (M.Ph.)--Chinese University of Hong Kong, 1988. / Bibliography: leaf 70.