Šiame darbe buvo nagrinėjamas dinaminis stochastinis stacionarus modelis aprašytas I.Carabenciov, I.Ermolaev, Ch.Freedman, M.Juillard, O.Kamenik, D.Korshunov, D.Laxton „A Small Quarterly Projection Model of the US Economy“ straipsnyje. Duomenis pateikė „Euromonitor International“ įmonė. Modelyje naudojami keturi Jungtinių Valstijų ekonomikos rodikliai: realus bendras vidaus produktas, nedarbo lygis, infliacija ir federalinių fondų palūkanų norma. Rodikliai stebimi 1994 m. І ketv. – 2009 m. ІІ ketv. laikotarpiu. Darbo tikslas – pakartoti „A Small Quarterly Projection Model of the US Economy“ straipsnio rezultatus. Buvo pasirinkti du programavimo paketai – Dynare ir Winbugs. Modelis buvo suprogramuotas dvejomis skirtingomis programomis, kurios remiasi Bajeso metodologija. Po to, gauti rezultatai buvo lyginami su straipsnyje pateiktais rezultatais. Atlikus visus skaičiavimus buvo gauti tokie rezultatai: su Dynare puikiai pavyko pakartoti modelio rezultatus. Su Winbugs programa gauti rezultatai nepilnai sutapo su straipsnyje pateiktais rezultatais. Iš to galima buvo padaryti išvada, kad Dynare programa labiau tinka dinaminių stochastinių stacionarių modelių vertinimui. Šio darbo rezultatai bus labai naudingi tyrimo planuotojams bei vykdytojams. Modelio rezultatai bus įtraukti į globalųjų modelį, kuris apjungs dar kelių valstybių modelius. / In the research paper a dynamic stochastic general equilibrium described in I.Carabenciov, I.Ermolaev, Ch.Freedman, M.Juillard, O.Kamenik, D.Korshunov, D.Laxton article „A Small Quarterly Projection Model of the US Economy“ was analyzed. The data for analysis was provided by “Euromonitor International“ company. The benchmark model has only four variables: real gross domestic product (GDP), unemployment rate, consumer price index, federal funds rate. The model is estimated over sample period from 1994QI till 2009QII. The aim of the research was to reiterate the results given in the article „A Small Quarterly Projection Model of the US Economy“. For this aim two programming packages were chosen – Dynare and Winbugs. The model was programmed using two different programs, both based on Bayesian methodology. Afterward the results were compared with results presented in the article. After all the calculations were done, the results were following: model was successfully repeated with Dynare. Although the results obtained with Winbugs program differed from the results given in the article. It therefore concluded that Dynare program is more suitable for the assessment of stationary stochastic dynamic models.

The purpose of this study was to compare 4 growth curve functions (Brody, Logistic, Gompertz, and Von Bertalanffy) in describing the weight change across age in Angus cow. A total of 1,705 weight-age records from birth to at least 3-year of age from 171 cows were collected. All the growth models were fitted as a nonlinear mixed model using NLMIXED procedure in SAS9.2 (REML Approach) and MCMC method through WinBUGS (Bayesian Approach). The goodness of fit of these four models was compared in terms of AIC, BIC, and DIC. The results show that the Gompertz model fitted the data best under REML Approach while the Brody model appeared to be the best model under Bayesian Approach. The Bayesian Approach provided more flexibility in setting up the mixed model and more reasonable estimates for all the growth models compared to the REML Approach. These results show some advantages of Bayesian nonlinear mixed modeling. / text

Bayesian statistical methods have long been computationally out of reach because the analysis often requires integration of high-dimensional functions. Recent advancements in computational tools to apply Markov Chain Monte Carlo (MCMC) methods are making Bayesian data analysis accessible for all statisticians. Two such computer tools are Win-BUGS and SASR 9.2's PROC MCMC. Bayesian methodology will be introduced through discussion of fourteen statistical examples with code and computer output to demonstrate the power of these computational tools in a wide variety of settings.

Multivariate ordinal response data, such as severity of pain, degree of disability, and satisfaction with a healthcare provider, are prevalent in many areas of research including public health, biomedical, and social science research. Ignoring the multivariate features of the response variables, that is, by not taking the correlation between the errors across models into account, may lead to substantially biased estimates and inference. In addition, such multivariate ordinal outcomes frequently exhibit a high percentage of zeros (zero inflation) at the lower end of the ordinal scales, as compared to what is expected under a multivariate ordinal distribution. Thus, zero inflation coupled with the multivariate structure make it difficult to analyze such data and properly interpret the results. Methods that have been developed to address the zero-inflated data are limited to univariate-logit or univariate-probit model, and extension to bivariate (or multivariate) probit models has been very limited to date. In this research, a latent variable approach was used to develop a Mixture Bivariate Zero-Inflated Ordered Probit (MBZIOP) model. A Bayesian MCMC technique was used for parameter estimation. A simulation study was then conducted to compare the performances of the estimators of the proposed model with two existing models. The simulation study suggested that for data with at least a moderate proportion of zeros in bivariate responses, the proposed model performed better than the comparison models both in terms of lower bias and greater accuracy (RMSE). Finally, the proposed method was illustrated with a publicly-available drug-abuse dataset to identify highly probable predictors of: (i) being a user/nonuser of marijuana, cocaine, or both; and (ii), conditional on user status, the level of consumption of these drugs. The results from the analysis suggested that older individuals, smokers, and people with a prior criminal background have a higher risk of being a marijuana only user, or being the user of both drugs. However, cocaine only users were predicted on the basis of being younger and having been engaged in the criminal-justice system. Given that an individual is a user of marijuana only, or user of both drugs, age appears to have an inverse effect on the latent level of consumption of marijuana as well as cocaine. Similarly, given that a respondent is a user of cocaine only, all covariates--age, involvement in criminal activities, and being of black race--are strong predictors of the level of cocaine consumption. The finding of older age being associated with higher drug consumption may represent a survival bias whereby previous younger users with high consumption may have been at elevated risk of premature mortality. Finally, the analysis indicated that blacks are likely to use less marijuana, but have a higher latent level of cocaine given that they are user of both drugs.

Several parasites that require two or more hosts to complete their life cycles are known to manipulate host behaviour, enhancing their transmission to the next host. The intertidal snail, Littorina sitkana, is host to a diverse assemblage of parasites dominated by trematodes. Trematodes often use snails as first intermediate host and vertebrates as definitive host. Trematode infections can affect host behaviours such as dispersal and foraging. I identified four sites in Barkley Sound that varied in trematode prevalence and species richness. I measured dispersal of snails at these sites and in the laboratory to assess effects of trematode infection on behaviour. I measured feeding rate under laboratory conditions. Trematode effects lowered snail grazing activity at three of the four sites studied, suggesting trematode infection lowers feeding rate of L. sitkana, potentially affecting algal composition of the intertidal zone. Infected male snails travelled longer distances in some sites but shorter distances in others. There was an almost significant effect of trematode infection on vertical displacement of L. sitkana in the field. I estimated survival rates on each site through intensive capture-mark-recapture experiments. There was differential survival among sites, but no negative correlation between survival estimates and trematode prevalence. / Graduate / 0718 / 0329 / 0472 / mayala@uvic.ca

En modelos econométricos clásicos de regresión binaria tradicionalmente se emplea la regresión logística, que se basa en el enlace simétrico logito. El propósito de este trabajo es presentar modelos de regresión binaria que, más bien, tengan enlaces asimétricos —aún no disponibles en software comercial—, cuando esta asimetría es más conveniente al investigador. Además, haciendo uso de un enfoque bayesiano con el programa WinBUGS, se implementa el programa BAYESPUCP, que facilitará la escritura de la sintaxis necesaria para implementar los modelos revisados. El BAYES-PUCP genera tanto las sintaxis de los modelos revisados así como de la estructura de los datos. El método es ilustrado con el caso de una muestra de agricultores que consideran la decisión de erradicar cultivos ilícitos de hoja de coca y, al mismo tiempo, se exploran factores asociados a esta decisión.

Chemotherapy is a method used to treat cancer but it has a number of side-effects. Research conducted by the Department of Chemical Engineering at BYU involves a new method of administering chemotherapy using ultrasound waves and water-soluble capsules. The goal is to reduce the side-effects by localizing the delivery of the medication. As part of this research, a two-factor experiment was conducted on rats to test if the water-soluble capsules and ultrasound waves by themselves have an effect on tumor growth or patient survival. Our project emphasizes the usage of Bayesian Hierarchical Models and Win-BUGS to jointly model the survival data and the longitudinal data—mass. The results of the joint analysis indicate that the use of ultrasound and water-soluble microcapsules have no negative effect on survival. In fact, there appears to be a positive effect on the survival since the rats in the ultrasound-capsule group had higher survival rates than the rats in other treatment groups. From these results, it does appear that the new technology involving ultrasound waves and microcapsules is a promising way to reduce the side-effects of chemotherapy. It is strongly advocated that the formulation of a joint model for any longitudinal and survival data be performed. For future work for the ultrasound-microcapsule data it is recommended that joint modeling of the mass, tumor volume, and survival data be conducted to obtain additional information.

Neste trabalho discutimos a análise bayesiana em modelos TRI (Teoria da Resposta ao Item) de três parâmetros com respostas binárias e ordinais, considerando a ligação probito. Em ambos os casos usamos técnicas baseadas em MCCM (método de Monte Carlo baseado em Cadeias de Markov) para estimação dos parâmetros dos itens. No modelo com respostas binárias, consideramos dois conjuntos de dados resultantes de provas com itens de múltipla-escolha. Para esses dados, foi feito um estudo da sensibilidade à escolha de distribuições a priori, além de uma análise das estimativas a posteriori para os parâmetros dos itens: discriminação, dificuldade e probabilidade de acerto ao acaso. Um terceiro conjunto de dados foi utilizado no estudo do modelo com respostas ordinais. Estes dados são provenientes de uma disciplina básica de estatística, onde a prova contêm itens dissertativos. As respostas foram classificadas nas categorias: certa, errada ou parcialmente certa. Utilizamos o programa WinBugs para a estimação dos parâmetros do modelo binário e a função MCMCordfactanal do programa R para estimar os parâmetros do modelo ordinal. Ambos os softwares são não proprietários e gratuitos (livres). / In this dissertation the bayesian analysis for three parameters IRT (Item Response Theory) models with binaries and ordinals responses, considering the probit model, was discussed. For both cases, binary and ordinal, techniques based on MCCM (Monte Carlo Markov Chain) were used to estimate the items parameters. For binary response model, was considered two data sets from tests with multipla choices items. For these two data sets, a sensibility study of the priori distributions choice was considered, and also, an analyses of a posteriori estimates of the items parameters: discrimination, difficulties and guessing. A third data set is used to ilustrate the ordinal response model. This come from an elementar statistical course, where a test with open items is considered. The responses are classified in the following categories: correct, wrong or partial correct. The WinBugs software was used to estimate the parameters for the binary model and, for the ordinal model was considered the function MCMCordfactanal from R program.

Neste trabalho discutimos a análise bayesiana em modelos TRI (Teoria da Resposta ao Item) de três parâmetros com respostas binárias e ordinais, considerando a ligação probito. Em ambos os casos usamos técnicas baseadas em MCCM (método de Monte Carlo baseado em Cadeias de Markov) para estimação dos parâmetros dos itens. No modelo com respostas binárias, consideramos dois conjuntos de dados resultantes de provas com itens de múltipla-escolha. Para esses dados, foi feito um estudo da sensibilidade à escolha de distribuições a priori, além de uma análise das estimativas a posteriori para os parâmetros dos itens: discriminação, dificuldade e probabilidade de acerto ao acaso. Um terceiro conjunto de dados foi utilizado no estudo do modelo com respostas ordinais. Estes dados são provenientes de uma disciplina básica de estatística, onde a prova contêm itens dissertativos. As respostas foram classificadas nas categorias: certa, errada ou parcialmente certa. Utilizamos o programa WinBugs para a estimação dos parâmetros do modelo binário e a função MCMCordfactanal do programa R para estimar os parâmetros do modelo ordinal. Ambos os softwares são não proprietários e gratuitos (livres). / In this dissertation the bayesian analysis for three parameters IRT (Item Response Theory) models with binaries and ordinals responses, considering the probit model, was discussed. For both cases, binary and ordinal, techniques based on MCCM (Monte Carlo Markov Chain) were used to estimate the items parameters. For binary response model, was considered two data sets from tests with multipla choices items. For these two data sets, a sensibility study of the priori distributions choice was considered, and also, an analyses of a posteriori estimates of the items parameters: discrimination, difficulties and guessing. A third data set is used to ilustrate the ordinal response model. This come from an elementar statistical course, where a test with open items is considered. The responses are classified in the following categories: correct, wrong or partial correct. The WinBugs software was used to estimate the parameters for the binary model and, for the ordinal model was considered the function MCMCordfactanal from R program.

Bioequivalence determines if two drugs are alike. The three kinds of bioequivalence are Average, Population, and Individual Bioequivalence. These Bioequivalence criteria can be evaluated using aggregate and disaggregate methods. Considerable work assessing bioequivalence in a frequentist method exists, but the advantages of Bayesian methods for Bioequivalence have been recently explored. Variance parameters are essential to any of theses existing Bayesian Bioequivalence metrics. Usually, the prior distributions for model parameters use either informative priors or vague priors. The Bioequivalence inference may be sensitive to the prior distribution on the variances. Recently, there have been questions about the routine use of inverse gamma priors for variance parameters. In this paper we examine the effect that changing the prior distribution of the variance parameters has on Bayesian models for assessing Bioequivalence and the carry-over effect. We explore our method with some real data sets from the FDA.