A classe de modelos de regressão beta tem sido estudada amplamente. Porém, para esta classe de modelos existem poucos trabalhos sobre a inclusão de efeitos aleatórios e a flexibilização da distribuição dos efeitos aleatórios, além de métodos de predição e de diagnóstico no ponto de vista dos efeitos aleatórios. Neste trabalho são propostos modelos de regressão beta com efeitos aleatórios normais e não normais para dados longitudinais. Os métodos de estimação de parâmetros e de predição dos efeitos aleatórios usados no trabalho são o método de máxima verossimilhança e o método do melhor preditor de Bayes empírico. Para aproximar a função de verossimilhança foi utilizada a quadratura de Gauss-Hermite. Métodos de seleção de modelos e análise de resíduos também foram propostos. Foi implementado o pacote BLMM no R para a realização de todos os procedimentos. O processo de estimação os parâmetros dos modelos e a distribuição empírica dos resíduos propostos foram analisados por meio de estudos de simulação. Foram consideradas várias distribuições para os efeitos aleatórios, valores para o número de indivíduos, número de observações por indivíduo e estruturas de variância-covariância para os efeitos aleatórios. Os resultados dos estudos de simulação mostraram que o processo de estimação obtém melhores resultados quando o número de indivíduos e o número de observações por indivíduo aumenta. Estes estudos também mostraram que o resíduo quantil aleatorizado segue uma distribuição aproximadamente normal. A metodologia apresentada é uma ferramenta completa para analisar dados longitudinais contínuos que estão restritos ao intervalo limitado (0; 1). / The class of beta regression models has been studied extensively. However, there are few studies on the inclusion of random effects and models with flexible random effects distributions besides prediction and diagnostic methods. In this work we proposed a beta regression models with normal and not normal random effects for longitudinal data. The maximum likelihood method and the empirical Bayes approach are used to obtain the estimates and the best prediction. Also, the Gauss-Hermite quadrature is used to approximate the likelihood function. Model selection methods and residual analysis were also proposed.We implemented a BLMM package in R to perform all procedures. The estimation procedure and the empirical distribution of residuals were analyzed through simulation studies considering differents random effects distributions, values for the number of individuals, number of observations per individual and covariance structures for the random effects. The results of simulation studies showed that the estimation procedure obtain better results when the number of individuals and the number of observations per individual increase. These studies also showed that the empirical distribution of the quantile randomized residual follows a normal distribution. The methodolgy presented is a tool for analyzing longitudinal data restricted to a interval (0; 1).

O objetivo deste trabalho foi avaliar a incorporação de funções geoestatísticas na matriz de variâncias e covariâncias residual no estudo de modelos lineares mistos a partir de um grupo de quatro experimentos de cana-de-açúcar, conduzidos na Guatemala nos seguintes locais: fazenda Limones - usina açucareira Pantaleón (LP), fazenda Bálsamo - usina açucareira Pantaleón (BP), área 1 da fazenda Limones - usina Madre Tierra (MT1) e área 2 da fazenda Limones - usina Madre Tierra (MT2). A variável resposta de interesse foi a produção de cana-de-açúcar por hectare, o delineamento utilizado nos quatro locais foi o casualizado em blocos, com cinco repetições e os mesmos seis tratamentos referentes a diferentes dosagens de um biorregulador (estimulante de crescimento). Em princípio, foram ajustados e comparados diversos modelos alternando-se o efeito de blocos, ora considerado fixo, ora aleatório, e a estrutura da matriz de variâncias e covariâncias (R), segundo os modelos exponencial, gaussiano e esférico. Estes modelos foram comparados, e os que admitem estruturas de dependência espacial se destacaram estatisticamente como os melhores, a partir do critério de Akaike (AIC), sendo então selecionados os modelos BFExp (blocos de efeito fixo e função exponencial na matriz R) e BAExpH (blocos de efeito aleatório, função exponencial para R e variâncias diferentes entre os locais). A seguir, foi realizada a estimação dos efeitos fixos e a predição dos efeitos aleatórios por meio do método da máxima verossimilhança restrita (REML) pois esta metodologia proporciona um menor viés para suas estimativas. As análises conjuntas nos dois modelos selecionados não apresentaram interação tratamentos versus locais, nem mesmo efeito de tratamentos significativos, não sendo aconselhado o desdobramento desta interação. O efeito de locais por sua vez, foi significativo apenas no modelo BAExpH, e detectou-se neste caso a superioridade do local BP em relação aos demais. Adicionalmente, os locais foram analisados individualmente, focando a comparação dos modelos e as análises de variâncias, contudo, assim como na análise conjunta, nos modelos escolhidos para cada local, os efeitos de tratamentos também não foram significativos. Gráficos de resíduos foram construídos e representaram bons ajustes para os modelos BFExp e BAExpH para descrever os dados deste grupo de experimentos. Por fim, foi realizado um estudo de simulação cujos resultados deram mais credibilidade e suporte para a importância e relevância de se verificar, por meio de comparações, a necessidade de uso de um modelo mais elaborado, que considere a possível existência de dependência espacial entre as observações. / The aim of this research was to evaluate the incorporation of geostatistical functions in the residual variances and covariances matrix in linear mixed models in a group of four experiments cane sugar conducted in four sites of Guatemala: farm Limones - Pantaleon sugar mill (LP), farm Bálsamo - Pantaleon sugar mill (BP), area 1 of the farm Limones - sugar mill Madre Tierra (MT1) and area 2 of the farm Limones - sugar mill Madre Tierra (MT2). Production of sugar cane was the interest variable analyzed at all locations, using the randomized block design with five replications and the same six treatments related to different doses of a plant growth regulator. Initially the models were adjusted and compared with alternating the blocks effect, sometimes considered fixed, sometimes random, and the structure of the variance and covariance matrix (R) according to the exponential, gaussian and spherical models. The models were compared, and, among them, those with spatial dependence structures stood out as the best statistically from the Akaike information criterion (AIC), and the selected modelos were the BFExp model (block as fixed effect and exponential function to R) and the BAExpH model (block as random effect, exponential function to R and different variances among the sites). After that, the estimation of fixed effects and prediction of random effects using the restricted maximum likelihood method (REML) were done, since this methodology provides a lower bias to their estimates. The joint analysis of both selected models showed no interaction between treatments and locals, even significant effect of treatments, not being advised the unfolding of this interaction. The effect of local was significant only in the BAExpH model, and detected in this case the superiority of the local BP in relation to the others. Additionally, individual sites were examined similarly to the previous case, through comparison of models and analysis of variance, however, treatment effects weren\'t significant too. Residual plots were constructed and represented satisfactory fit of the models to describe the data in all cases studied. Finally, a simulation study showed results with more credibility and support for the importance and relevance of verifying, through comparisons, the need to use a more structured model that considers the possible existence of spatial dependence among observations.

Experimentos cujas variaveis respostas s~ ao proporcoes ou contagens, sao muito comuns nas diversas areas do conhecimento, principalmente na area agricola. Na analise desses experimentos, utiliza-se a teoria de modelos lineares generalizados, bastante difundida (McCullagh & Nelder, 1989; Demetrio, 2001), em que as respostas sao independentes. Caso a variancia estimada seja maior do que a esperada, estima-se o parametro de dispersao, incluindo-o no processo de estimaçao dos parametros. Quando a variavel resposta e observada ao longo do tempo, pode haver uma correlacao entre as observacoes e isso tem que ser levado em consideracao na estimacao dos parametros. Uma forma de se trabalhar essa correlacao e aplicando a metodologia de equacoes de estimacao generalizada (EEG), discutida por Liang & Zeger (1986), embora, neste caso, o interesse esteja nas estimativas dos efeitos fixos e a inclusao da matriz de correlacao de trabalho sirva para se obter um melhor ajuste. Uma outra alternativa e a inclusao, no preditor linear, de um efeito latente para captar variabilidades nao consideradas no modelo e que podem in uenciar nos resultados. No presente trabalho, usa-se uma forma combinada de efeito aleatorio e parametro de dispersao, incluidos conjuntamente na estimacao dos parametros. Essa metodologia e aplicada a um conjunto de dados obtidos de um experimento com camu-camu, com objetivo de se avaliarem quais os melhores metodos de enxertia e tipos de porta-enxertos que podem ser utilizados, atraves da proporcao de pegamentos da muda. Varios modelos sao ajustados, desde o modelo em parcelas subdivididas (supondo independencia), ate o modelo em que se considera o parametro de dispersao e efeito aleatorio conjuntamente. Ha evidencias de que o modelo em que se inclui o efeito aleatorio e o parametro de dispersao, conjuntamente, resultam em melhores estimativas dos parametros. Outro conjunto de dados longitudinais, com milho transgenico MON810, em que a variavel resposta e o numero de lagartas (Spodoptera frugiperda), e utilizado. Neste caso, devido ao excesso de respostas zero, emprega-se o modelo de regressao Poisson in acionado de zeros (ZIP), alem do modelo Poisson padrao, em que as observacoes sao consideradas independentes, e do modelo Poisson in acionado de zeros com efeito aleatorio. Os resultados mostram que o efeito aleatorio incluido no preditor foi nao significativo e, assim, o modelo adotado e o modelo de regressao Poisson in acionado de zeros. Os resultados foram obtidos usando-se os procedimentos NLMIXED, GENMOD e GPLOT do SAS - Statistical Analysis System, versao 8.2. / Experiments which response variables are proportions or counts are very common in several research areas, specially in the area of agriculture. The theory of generalized linear models, well difused (McCullagh & Nelder, 1989; Demetrio, 2001), is used for analyzing these experiments where the responses are independent. If the estimated variance is greater than the expected variance, the dispersion parameter is estimated including it on the parameter estimation process. When the response variable is observed over time a correlation among observations might occur and it should be taken into account in the parameter estimation. A way of dealing with this correlation is applying the methodology of generalized estimating equations (GEEs) discussed by Liang & Zeger (1986) although, in this case, the interest is on the estimates of the xed efect being the inclusion of a working correlation matrix useful to obtain more accurate estimates. Another alternative is the inclusion of a latent efect in the linear predictor to explain variabilities not considered in the model that might in uence the results. In this work the random efect and the dispersion parameter are combined and included together in the parameter estimation. Such methodology is applied to a data set obtained from an experiment realized with camu-camu to evaluate, through proportion of grafting well successful of seedling, which kind of grafting and understock are suitable to be used. Several models are fitted, since the split plot model (with independence assumption) up to the model where the dispersion parameter and the random efect are considered together. There is evidence that the model including the random efect and the dispersion parameter together, produce better estimates of the parameters. Another longitudinal data set used here comes from an experiment realized with the MON810 transgenic corn where the response variable is the number of caterpillars (Spodoptera frugiperda). In this case, due to the excessive number of zeros obtained, the zero in ated Poisson regression model (ZIP) is used in addition to the standard Poisson model, where observations are considered independent, and the zero in ated Poisson regression model with random efect. The results show that the random efect included in the linear predictor was not significant and, therefore, the adopted model is the zero in ated Poisson regression model. The results were obtained using the procedures NLMIXED, GENMOD and GPLOT available on SAS - Statistical Analysis System, version 8.2.

A classe de modelos de regressão beta tem sido estudada amplamente. Porém, para esta classe de modelos existem poucos trabalhos sobre a inclusão de efeitos aleatórios e a flexibilização da distribuição dos efeitos aleatórios, além de métodos de predição e de diagnóstico no ponto de vista dos efeitos aleatórios. Neste trabalho são propostos modelos de regressão beta com efeitos aleatórios normais e não normais para dados longitudinais. Os métodos de estimação de parâmetros e de predição dos efeitos aleatórios usados no trabalho são o método de máxima verossimilhança e o método do melhor preditor de Bayes empírico. Para aproximar a função de verossimilhança foi utilizada a quadratura de Gauss-Hermite. Métodos de seleção de modelos e análise de resíduos também foram propostos. Foi implementado o pacote BLMM no R para a realização de todos os procedimentos. O processo de estimação os parâmetros dos modelos e a distribuição empírica dos resíduos propostos foram analisados por meio de estudos de simulação. Foram consideradas várias distribuições para os efeitos aleatórios, valores para o número de indivíduos, número de observações por indivíduo e estruturas de variância-covariância para os efeitos aleatórios. Os resultados dos estudos de simulação mostraram que o processo de estimação obtém melhores resultados quando o número de indivíduos e o número de observações por indivíduo aumenta. Estes estudos também mostraram que o resíduo quantil aleatorizado segue uma distribuição aproximadamente normal. A metodologia apresentada é uma ferramenta completa para analisar dados longitudinais contínuos que estão restritos ao intervalo limitado (0; 1). / The class of beta regression models has been studied extensively. However, there are few studies on the inclusion of random effects and models with flexible random effects distributions besides prediction and diagnostic methods. In this work we proposed a beta regression models with normal and not normal random effects for longitudinal data. The maximum likelihood method and the empirical Bayes approach are used to obtain the estimates and the best prediction. Also, the Gauss-Hermite quadrature is used to approximate the likelihood function. Model selection methods and residual analysis were also proposed.We implemented a BLMM package in R to perform all procedures. The estimation procedure and the empirical distribution of residuals were analyzed through simulation studies considering differents random effects distributions, values for the number of individuals, number of observations per individual and covariance structures for the random effects. The results of simulation studies showed that the estimation procedure obtain better results when the number of individuals and the number of observations per individual increase. These studies also showed that the empirical distribution of the quantile randomized residual follows a normal distribution. The methodolgy presented is a tool for analyzing longitudinal data restricted to a interval (0; 1).

Nós consideramos decomposições de estatísticas $U$ para obter testes para componentes de variância. As distribuições assintóticas das estatísticas de testes sob a hipótese nula são obtidas supondo apenas a existência do quarto momento do erro condicional e do segundo momento dos efeitos aleatórios. Isso permite sua utilização em uma classe bastante ampla de distribuições. Sob a suposição adicional de existência do quarto momento dos efeitos aleatórios, obtemos também a distribuição assintótica das estatísticas sob uma seqüência de hipóteses alternativas locais. Comparamos a eficiência dos testes propostos com aqueles dos testes clássicos, obtidos sob suposição de normalidade, por meio de estudos de simu-lação. Os testes propostos se mostram mais adequados nas situações em que a amostra é de tamanho moderado ou grande, independentemente da distribuição das fontes de variação, e nas situações em que existe fortes afastamentos da normalidade. / We consider decompositions of U-statistics to obtain tests for null variance components in linear mixed models. Their asymptotic distributions under the null hypothesis are obtained only assuming the existence of the first four moments of the conditional error distribution and the existence of the first two moments of the random effects distribution. Thus, the proposed U-tests may be employed in a large class of models. Under the additional assumption of the existence of the fourth moment of the distribution of the random effects, we also obtain the asymptotic distribution of the U-tests under a sequence of local hypothesis. We compare their efficiency with that of classical tests derived under the assumption of normality, through simulation studies. The proposed tests are more efficient in situations where the sample size is moderate or large, independently of the distribution of the sources of variation; they also perform better in situations where the underlying distributions are far from normal.

Para analisar dados binários oriundos de uma estrutura hierárquica com dois níveis (por exemplo, aluno e escola), uma alternativa bastante utilizada é a suposição da distribuição binomial para as unidades experimentais do primeiro nível (aluno) condicionalmente a um efeito aleatório proveniente de uma distribuição normal para as unidades do segundo nível (escola). Neste trabalho, propõe-se a adição de um efeito aleatório normal no primeiro nível de um modelo linear generalizado hierárquico binomial para contemplar uma possível variabilidade extra-binomial decorrente da dependência entre os ensaios de Bernoulli de um mesmo indivíduo. Obtém-se o processo de estimação por máxima verossimilhança para este modelo a partir da verossimilhança marginal dos dados, após uma dupla aplicação do método de quadratura de Gauss-Hermite adaptativa como aproximação para as integrais dos efeitos aleatórios. Realiza-se um estudo de simulação para contrastar propriedades inferenciais do modelo aspirante com o modelo linear generalizado binomial, um modelo de quase-verossimilhança e o tradicional modelo linear generalizado hierárquico em dois níveis. / A common alternative when analyzing binary data originated from a two-level hierarchical structure (for instance, student and school) is to assume a binomial distribution for the experimental units of the first level (student) conditionally to a normal random effect for the second level units (school). In this work, we propose the inclusion of a second normal random effect in the first level to contemplate a possible extra-binomial variability due to the dependence among the Bernoulli trials in the same individual. We obtain the maximum likelihood estimation process for this hierarchical model starting from the marginal likelihood of the data, after a double application of the adaptive Gauss-Hermite quadrature as an approximation of the integrals of the random effects. We conduct a simulation study to compare the inferential properties of the advocated model with the generalized linear (binomial) model, a quasi-likelihood model and the usual two-level hierarchical generalized linear model.

One basic problem in statistical sciences is to understand the relationships among multivariate outcomes. Although it remains an important tool and is widely applicable, the regression analysis is limited by the basic setup that requires to identify one dimension of the outcomes as the primary measure of interest (the "dependent" variable) and other dimensions as supporting this variable (the "explanatory" variables). There are situations where this relationship is not of primary interest. For example, in actuarial sciences, one might be interested to see the dependence between annual claim numbers of a policyholder and its impact on the premium or the dependence between the claim amounts and the expenses related to them. In such cases the normality hypothesis fails, thus Pearson's correlation or concepts based on linearity are no longer the best ones to be used. Therefore, in order to quantify the dependence between non-normal outcomes one needs different statistical tools, such as, for example, the dependence concepts and the copulas. This thesis is devoted to modelling dependence with applications in actuarial sciences and is divided in two parts: the first one concerns dependence in frequency credibility models and the second one dependence between continuous outcomes. In each part of the thesis we resort to different tools, the stochastic orderings (which arise from the dependence concepts), and copulas, respectively. During the last decade of the 20th century, the world of insurance was confronted with important developments of the a posteriori tarification, especially in the field of credibility. This was dued to the easing of insurance markets in the European Union, which gave rise to an advanced segmentation. The first important contribution is due to Dionne & Vanasse (1989), who proposed a credibility model which integrates a priori and a posteriori information on an individual basis. These authors introduced a regression component in the Poisson counting model in order to use all available information in the estimation of accident frequency. The unexplained heterogeneity was then modeled by the introduction of a latent variable representing the influence of hidden policy characteristics. The vast majority of the papers appeared in the actuarial literature considered time-independent (or static) heterogeneous models. Noticeable exceptions include the pioneering papers by Gerber & Jones (1975), Sundt (1988) and Pinquet, Guillén & Bolancé (2001, 2003). The allowance for an unknown underlying random parameter that develops over time is justified since unobservable factors influencing the driving abilities are not constant. One might consider either shocks (induced by events like divorces or nervous breakdown, for instance) or continuous modifications (e.g. due to learning effect). In the first part we study the recently introduced models in the frequency credibility theory, which can be seen as models of time series for count data, adapted to actuarial problems. More precisely we will examine the kind of dependence induced among annual claim numbers by the introduction of random effects taking unexplained heterogeneity, when these random effects are static and time-dependent. We will also make precise the effect of reporting claims on the a posteriori distribution of the random effect. This will be done by establishing some stochastic monotonicity property of the a posteriori distribution with respect to the claims history. We end this part by considering different models for the random effects and computing the a posteriori corrections of the premiums on basis of a real data set from a Spanish insurance company. Whereas dependence concepts are very useful to describe the relationship between multivariate outcomes, in practice (think for instance to the computation of reinsurance premiums) one need some statistical tool easy to implement, which incorporates the structure of the data. Such tool is the copula, which allows the construction of multivariate distributions for given marginals. Because copulas characterize the dependence structure of random vectors once the effect of the marginals has been factored out, identifying and fitting a copula to data is not an easy task. In practice, it is often preferable to restrict the search of an appropriate copula to some reasonable family, like the archimedean one. Then, it is extremely useful to have simple graphical procedures to select the best fitting model among some competing alternatives for the data at hand. In the second part of the thesis we propose a new nonparametric estimator for the generator, that takes into account the particularity of the data, namely censoring and truncation. This nonparametric estimation then serves as a benchmark to select an appropriate parametric archimedean copula. This selection procedure will be illustrated on a real data set.

Nearly 80% of children treated for cancer are expected to survive, but not without cost. Survivors face unprecedented challenges associated with long-term consequences of treatment, also called late effects. Approximately half of children treated for cancer are at risk for experiencing cognitive late effects, which typically emerge several years post diagnosis. The nature and extent of cognitive late effects appear to be developmental and related to patient, disease, and treatment variables. However, the relationships between these variables is not well understood because there have been few prospective and longitudinal studies that report on the contributions of these variables over time. This dissertation examined the effects of patient, disease, and treatment variables, as well as their interactions over time on neurocognitive functioning in childhood cancer survivors. It comprises part of a large prospective, randomized clinical trial designed to examine changes in cognitive function over three years as a function of different levels of monitoring of school-based intervention based on individual educational plans (IEPs). This dissertation uniquely contributed a new measure (the Treatment Intensity Rating Scale) that was used to systematically classify treatment severity across different types of cancer and cancer treatments. Participants included 61 children ages 7 to 12 years at enrollment who were two to five years from completion of treatment for a brain tumor, leukemia, or lymphoma. Participants received yearly neuropsychological evaluations for a follow-up period of 3 years. Results of these evaluations were used to develop IEPs. Participants were randomized to have their IEPs monitored on a quarterly or annual basis for the duration of the study. Contrary to the progressive decline in neurocognitive functioning that is typically anticipated in pediatric cancer survivors, analyses revealed relative stability of performance on neurocognitive measures over time. Higher neurocognitive performance was noted in children whose IEPs were monitored more frequently versus less frequently. Results also supported gender-specific risk for late effects, with lower performance on select neurocognitive measures in females compared to males. Results of this study provide encouraging evidence of the positive effects of school-based interventions and their close monitoring. This has important implications for quality of life as these children survive well beyond childhood into adulthood.

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.

In an experiment to understand colon carcinogenesis, all animals were exposed to a carcinogen while half the animals were also exposed to radiation. Spatially, we measured the existence of aberrant crypt foci (ACF), namely morphologically changed colonic crypts that are known to be precursors of colon cancer development. The biological question of interest is whether the locations of these ACFs are spatially correlated: if so, this indicates that damage to the colon due to carcinogens and radiation is localized. Statistically, the data take the form of binary outcomes (corresponding to the existence of an ACF) on a regular grid. We develop score??type methods based upon the Matern and conditionally autoregression (CAR) correlation models to test for the spatial correlation in such data, while allowing for nonstationarity. Because of a technical peculiarity of the score??type test, we also develop robust versions of the method. The methods are compared to a generalization of Moran??s test for continuous outcomes, and are shown via simulation to have the potential for increased power. When applied to our data, the methods indicate the existence of spatial correlation, and hence indicate localization of damage. Assuming that there are correlations in the locations of the ACF, the questions are how great are these correlations, and whether the correlation structures di?er when an animal is exposed to radiation. To understand the extent of the correlation, we cast the problem as a spatial binary regression, where binary responses arise from an underlying Gaussian latent process. We model these marginal probabilities of ACF semiparametrically, using ?xed-knot penalized regression splines and single-index models. We ?t the models using pairwise pseudolikelihood methods. Assuming that the underlying latent process is strongly mixing, known to be the case for many Gaussian processes, we prove asymptotic normality of the methods. The penalized regression splines have penalty parameters that must converge to zero asymptotically: we derive rates for these parameters that do and do not lead to an asymptotic bias, and we derive the optimal rate of convergence for them. Finally, we apply the methods to the data from our experiment.