Dissertations / Theses on the topic 'Parametric regression models'
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Li, Lingzhu. "Model checking for general parametric regression models." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/654.
Full textChen, Chunxia. "Semi-parametric estimation in Tobit regression models." Kansas State University, 2013. http://hdl.handle.net/2097/15300.
Full textDepartment of Statistics
Weixing Song
In the classical Tobit regression model, the regression error term is often assumed to have a zero mean normal distribution with unknown variance, and the regression function is assumed to be linear. If the normality assumption is violated, then the commonly used maximum likelihood estimate becomes inconsistent. Moreover, the likelihood function will be very complicated if the regression function is nonlinear even the error density is normal, which makes the maximum likelihood estimation procedure hard to implement. In the full nonparametric setup when both the regression function and the distribution of the error term [epsilon] are unknown, some nonparametric estimators for the regression function has been proposed. Although the assumption of knowing the distribution is strict, it is a widely adopted assumption in Tobit regression literature, and is also confirmed by many empirical studies conducted in the econometric research. In fact, a majority of the relevant research assumes that [epsilon] possesses a normal distribution with mean 0 and unknown standard deviation. In this report, we will try to develop a semi-parametric estimation procedure for the regression function by assuming that the error term follows a distribution from a class of 0-mean symmetric location and scale family. A minimum distance estimation procedure for estimating the parameters in the regression function when it has a specified parametric form is also constructed. Compare with the existing semiparametric and nonparametric methods in the literature, our method would be more efficient in that more information, in particular the knowledge of the distribution of [epsilon], is used. Moreover, the computation is relative inexpensive. Given lots of application does assume that [epsilon] has normal or other known distribution, the current work no doubt provides some more practical tools for statistical inference in Tobit regression model.
Delgado, Carlos Alberto Cardozo. "Semi-parametric generalized log-gamma regression models." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-15032018-185352/.
Full textO objetivo central do trabalho é proporcionar ferramentas estatísticas para modelos de regressão semiparamétricos quando os erros seguem distribução log-gamma generalizada na presença de observações censuradas ou não censuradas. A estimação paramétrica e não paramétrica são realizadas através dos procedimentos Newton - Raphson, escore de Fisher e Backfitting (Gauss - Seidel). As propriedades assintóticas dos estimadores de máxima verossimilhança penalizada são estudadas em forma analítica, bem como através de simulações. Alguns procedimentos de diagnóstico são desenvolvidos, tais como resíduos tipo componente do desvio e resíduo quantílico, bem como medidas de influ\\^encia local sob alguns esquemas usuais de perturbação. Todos procedimentos do presente trabalho são implementados no ambiente computacional R, o pacote sglg é desenvolvido, assim como algumas aplicações a dados reais são apresentadas.
Peluso, Alina. "Novel regression models for discrete response." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/15581.
Full textShadat, Wasel Bin. "Specification testing of Garch regression models." Thesis, University of Manchester, 2011. https://www.research.manchester.ac.uk/portal/en/theses/specification-testing-of-garch-regression-models(56c218db-9b91-4d8c-bf26-8377ab185c71).html.
Full textEspigolan, Rafael [UNESP]. "Parametric and semi-parametric models for predicting genomic breeding values of complex traits in Nelore cattle." Universidade Estadual Paulista (UNESP), 2017. http://hdl.handle.net/11449/149846.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
O melhoramento genético animal visa melhorar a produtividade econômica das futuras gerações de espécies domésticas por meio da seleção. A maioria das características de interesse econômico na pecuária é de expressão quantitativa e complexa, isto é, são influenciadas por vários genes e afetadas por fatores ambientais. As análises estatísticas de informações de fenótipo e pedigree permite estimar os valores genéticos dos candidatos à seleção com base no modelo infinitesimal. Uma grande quantidade de dados genômicos está atualmente disponível para a identificação e seleção de indivíduos geneticamente superiores com o potencial de aumentar a acurácia de predição dos valores genéticos e, portanto, a eficiência dos programas de melhoramento genético animal. Vários estudos têm sido conduzidos com o objetivo de identificar metodologias apropriadas para raças e características específicas, o que resultará em estimativas de valores genéticos genômicos (GEBVs) mais acurados. Portanto, o objetivo deste estudo foi verificar a possibilidade de aplicação de modelos semiparamétricos para a seleção genômica e comparar a habilidade de predição com os modelos paramétricos para dados reais (características de carcaça, qualidade da carne, crescimento e reprodutiva) e simulados. As informações fenotípicas e de pedigree utilizadas foram fornecidas por onze fazendas pertencentes a quatro programas de melhoramento genético animal. Para as características de carcaça e qualidade da carne, o banco de dados continha 3.643 registros para área de olho de lombo (REA), 3.619 registros para espessura de gordura (BFT), 3.670 registros para maciez da carne (TEN) e 3.378 observações para peso de carcaça quente (HCW). Um total de 825.364 registros para peso ao sobreano (YW) e 166.398 para idade ao primeiro parto (AFC) foi utilizado para as características de crescimento e reprodutiva. Genótipos de 2.710, 2.656, 2.749, 2.495, 4.455 e 1.760 animais para REA, BFT, TEN, HCW, YW e AFC foram disponibilizados, respectivamente. Após o controle de qualidade, restaram dados de, aproximadamente, 450.000 polimorfismos de base única (SNP). Os modelos de análise utilizados foram BLUP genômico (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) e as abordagens semiparamétricas Reproducing Kernel Hilbert Spaces (RKHS) e Kernel Averaging (KA). Para cada característica foi realizada uma validação cruzada composta por cinco “folds” e replicada aleatoriamente trinta vezes. Os modelos estatísticos foram comparados em termos do erro do quadrado médio (MSE) e acurácia de predição (ACC). Os valores de ACC variaram de 0,39 a 0,40 (REA), 0,38 a 0,41 (BFT), 0,23 a 0,28 (TEN), 0,33 a 0,35 (HCW), 0,36 a 0,51 (YW) e 0,49 a 0,56 (AFC). Para todas as características, os modelos GBLUP e BL apresentaram acurácias de predição similares. Para REA, BFT e HCW, todos os modelos apresentaram ACC similares, entretanto a regressão RKHS obteve o melhor ajuste comparado ao KA. Para características com maior quantidade de registros fenotípicos comparada ao número de animais genotipados (YW e AFC) o modelo ssGBLUP é indicado. Considerando o desempenho geral, para todas as características estudadas, a regressão RKHS é, particularmente, uma alternativa interessante para a aplicação na seleção genômica, especialmente para características de baixa herdabilidade. No estudo de simulação, genótipos, pedigree e fenótipos para quatro características (A, B, C e D) foram simulados utilizando valores de herdabilidade baseados nos obtidos com os dados reais (0,09, 0,12, 0,36 e 0,39 para cada característica, respectivamente). O genoma simulado consistiu de 735.293 marcadores e 1.000 QTLs distribuídos aleatoriamente por 29 pares de autossomos, com comprimento variando de 40 a 146 centimorgans (cM), totalizando 2.333 cM. Assumiu-se que os QTLs explicavam 100% da variação genética. Considerando as frequências do alelo menor maiores ou iguais a 0,01, um total de 430.000 marcadores foram selecionados aleatoriamente. Os fenótipos foram obtidos pela soma dos resíduos (aleatoriamente amostrados de uma distribuição normal com média igual a zero) aos valores genéticos verdadeiros, e todo o processo de simulação foi replicado 10 vezes. A ACC foi calculada por meio da correlação entre o valor genético genômico estimado e o valor genético verdadeiro, simulados da 12a a 15a geração. A média do desequilíbrio de ligação, medido entre os pares de marcadores adjacentes para todas as características simuladas foi de 0,21 para as gerações recentes (12a, 13a e 14a), e 0,22 para a 15a geração. A ACC para as características simuladas A, B, C e D variou de 0,43 a 0,44, 0,47 a 0,48, 0,80 a 0,82 e 0,72 a 0,73, respectivamente. Diferentes metodologias de seleção genômica implementadas neste estudo mostraram valores similares de acurácia de predição, e o método mais adequado é dependente da característica explorada. Em geral, as regressões RKHS obtiveram melhor desempenho em termos de ACC com menor valor de MSE em comparação com os outros modelos.
Animal breeding aims to improve economic productivity of future generations of domestic species through selection. Most of the traits of economic interest in livestock have a complex and quantitative expression i.e. are influenced by a large number of genes and affected by environmental factors. Statistical analysis of phenotypes and pedigree information allows estimating the breeding values of the selection candidates based on infinitesimal model. A large amount of genomic data is now available for the identification and selection of genetically superior individuals with the potential to increase the accuracy of prediction of genetic values and thus, the efficiency of animal breeding programs. Numerous studies have been conducted in order to identify appropriate methodologies to specific breeds and traits, which will result in more accurate genomic estimated breeding values (GEBVs). Therefore, the objective of this study was to verify the possibility of applying semi-parametric models for genomic selection and to compare their ability of prediction with those of parametric models for real (carcass, meat quality, growth and reproductive traits) and simulated data. The phenotypic and pedigree information used were provided by farms belonging to four animal breeding programs which represent eleven farms. For carcass and meat quality traits, the data set contained 3,643 records for rib eye area (REA), 3,619 records for backfat thickness (BFT), 3,670 records for meat tenderness (TEN) and 3,378 observations for hot carcass weight (HCW). A total of 825,364 records for yearling weight (YW) and 166,398 for age at first calving (AFC) were used as growth and reproductive traits of Nelore cattle. Genotypes of 2,710, 2,656, 2,749, 2,495, 4,455 and 1,760 animals were available for REA, BFT, TEN, HCW, YW and AFC, respectively. After quality control, approximately 450,000 single nucleotide polymorphisms (SNP) remained. Methods of analysis were genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP), Bayesian LASSO (BL) and the semi-parametric approaches Reproducing Kernel Hilbert Spaces (RKHS) regression and Kernel Averaging (KA). A five-fold cross-validation with thirty random replicates was carried out and models were compared in terms of their prediction mean squared error (MSE) and accuracy of prediction (ACC). The ACC ranged from 0.39 to 0.40 (REA), 0.38 to 0.41 (BFT), 0.23 to 0.28 (TEN), 0.33 to 0.35 (HCW), 0.36 to 0.51 (YW) and 0.49 to 0.56 (AFC). For all traits, the GBLUP and BL models showed very similar prediction accuracies. For REA, BFT and HCW, models provided similar prediction accuracies, however RKHS regression had the best fit across traits considering multiple-step models and compared to KA. For traits which have a higher number of animals with phenotypes compared to the number of those with genotypes (YW and AFC), the ssGBLUP is indicated. Judged by overall performance, across all traits, the RKHS regression is particularly appealing for application in genomic selection, especially for low heritability traits. Simulated genotypes, pedigree, and phenotypes for four traits A, B, C and D were obtained using heritabilities based on real data (0.09, 0.12, 0.36 and 0.39 for each trait, respectively). The simulated genome consisted of 735,293 markers and 1,000 QTLs randomly distributed over 29 pairs of autosomes, with length varying from 40 to 146 centimorgans (cM), totaling 2,333 cM. It was assumed that QTLs explained 100% of genetic variance. Considering Minor Allele Frequencies greater or equal to 0.01, a total of 430,000 markers were randomly selected. The phenotypes were generated by adding residuals, randomly drawn from a normal distribution with mean equal to zero, to the true breeding values and all simulation process was replicated 10 times. ACC was quantified using correlations between the predicted genomic breeding value and true breeding values simulated for the generations of 12 to 15. The average linkage disequilibrium, measured between pairs of adjacent markers for all simulated traits was 0.21 for recent generations (12, 13 and 14), and 0.22 for generation 15. The ACC for simulated traits A, B, C and D ranged from 0.43 to 0.44, 0.47 to 0.48, 0.80 to 0.82 and 0.72 to 0.73, respectively. Different genomic selection methodologies implemented in this study showed similar accuracies of prediction, and the optimal method was sometimes trait dependent. In general, RKHS regressions were preferable in terms of ACC and provided smallest MSE estimates compared to other models.
FAPESP: 2014/00779-0
FAPESP: 2015/13084-3
Wang, Sejong. "Three nonparametric specification tests for parametric regression models : the kernel estimation approach." Connect to resource, 1994. http://rave.ohiolink.edu/etdc/view.cgi?acc%5Fnum=osu1261492759.
Full textMostafa, Abdelelah M. "Regression approach to software reliability models." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001648.
Full textLäuter, Henning. "Estimation in partly parametric additive Cox models." Universität Potsdam, 2003. http://opus.kobv.de/ubp/volltexte/2011/5150/.
Full textMasiulaitytė, Inga. "Regression and degradation models in reliability theory and survival analysis." Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2010. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2010~D_20100527_134956-15325.
Full textDaktaro disertacijos tyrimo objektai yra rezervuotos sistemos ir degradaciniai modeliai. Norint užtikrinti svarbių sistemos elementų aukštą patikimumą, naudojami jų rezerviniai elementai, kurie gali būti įjungiami sugedus šiems pagrindiniams elementams. Rezerviniai elementai gali funkcionuoti skirtinguose režimuose: „karštame“, „šaltame“ arba „šiltame“. Disertacijoje yra nagrinėjamos sistemos su „šiltai“ rezervuotais elementais. Darbe suformuluojama rezervinio elemento „sklandaus įjungimo“ hipotezė ir konstruojami statistiniai kriterijai šiai hipotezei tikrinti. Nagrinėjami neparametrinio ir parametrinio taškinio bei intervalinio vertinimo uždaviniai. Disertacijoje nagrinėjami pakankamai bendri degradacijos modeliai, kurie aprašo elementų gedimų intensyvumą kaip funkciją kiek naudojamų apkrovų, tiek ir degradacijos lygio, kuri savo ruožtu modeliuojama naudojant stochastinius procesus.
Pawar, Roshan. "Predicting bid prices in construction projects using non-parametric statistical models." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-1464.
Full textHelvaci, Aziz. "Comparison Of Parametric Models For Conceptual Duration Estimation Of Building Projects." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609759/index.pdf.
Full textDas, Debasish. "Bayesian Sparse Regression with Application to Data-driven Understanding of Climate." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/313587.
Full textPh.D.
Sparse regressions based on constraining the L1-norm of the coefficients became popular due to their ability to handle high dimensional data unlike the regular regressions which suffer from overfitting and model identifiability issues especially when sample size is small. They are often the method of choice in many fields of science and engineering for simultaneously selecting covariates and fitting parsimonious linear models that are better generalizable and easily interpretable. However, significant challenges may be posed by the need to accommodate extremes and other domain constraints such as dynamical relations among variables, spatial and temporal constraints, need to provide uncertainty estimates and feature correlations, among others. We adopted a hierarchical Bayesian version of the sparse regression framework and exploited its inherent flexibility to accommodate the constraints. We applied sparse regression for the feature selection problem of statistical downscaling of the climate variables with particular focus on their extremes. This is important for many impact studies where the climate change information is required at a spatial scale much finer than that provided by the global or regional climate models. Characterizing the dependence of extremes on covariates can help in identification of plausible causal drivers and inform extremes downscaling. We propose a general-purpose sparse Bayesian framework for covariate discovery that accommodates the non-Gaussian distribution of extremes within a hierarchical Bayesian sparse regression model. We obtain posteriors over regression coefficients, which indicate dependence of extremes on the corresponding covariates and provide uncertainty estimates, using a variational Bayes approximation. The method is applied for selecting informative atmospheric covariates at multiple spatial scales as well as indices of large scale circulation and global warming related to frequency of precipitation extremes over continental United States. Our results confirm the dependence relations that may be expected from known precipitation physics and generates novel insights which can inform physical understanding. We plan to extend our model to discover covariates for extreme intensity in future. We further extend our framework to handle the dynamic relationship among the climate variables using a nonparametric Bayesian mixture of sparse regression models based on Dirichlet Process (DP). The extended model can achieve simultaneous clustering and discovery of covariates within each cluster. Moreover, the a priori knowledge about association between pairs of data-points is incorporated in the model through must-link constraints on a Markov Random Field (MRF) prior. A scalable and efficient variational Bayes approach is developed to infer posteriors on regression coefficients and cluster variables.
Temple University--Theses
Schildcrout, Jonathan Scott. "Marginal modeling of longitudinal, binary response data : semiparametric and parametric estimation with long response series and an efficient outcome dependent sampling design /." Thesis, Connect to this title online; UW restricted, 2004. http://hdl.handle.net/1773/9540.
Full textChau, Thi Tuyet Trang. "Non-parametric methodologies for reconstruction and estimation in nonlinear state-space models." Thesis, Rennes 1, 2019. http://www.theses.fr/2019REN1S010/document.
Full textThe amount of both observational and model-simulated data within the environmental, climate and ocean sciences has grown at an accelerating rate. Observational (e.g. satellite, in-situ...) data are generally accurate but still subject to observational errors and available with a complicated spatio-temporal sampling. Increasing computer power and understandings of physical processes have permitted to advance in models accuracy and resolution but purely model driven solutions may still not be accurate enough. Filtering and smoothing (or sequential data assimilation methods) have developed to tackle the issues. Their contexts are usually formalized under the form of a space-state model including the dynamical model which describes the evolution of the physical process (state), and the observation model which describes the link between the physical process and the available observations. In this thesis, we tackle three problems related to statistical inference for nonlinear state-space models: state reconstruction, parameter estimation and replacement of the dynamic model by an emulator constructed from data. For the first problem, we will introduce an original smoothing algorithm which combines the Conditional Particle Filter (CPF) and Backward Simulation (BS) algorithms. This CPF-BS algorithm allows for efficient exploration of the state of the physical variable, sequentially refining exploration around trajectories which best meet the constraints of the dynamic model and observations. We will show on several toy models that, at the same computation time, the CPF-BS algorithm gives better results than the other CPF algorithms and the stochastic EnKS algorithm which is commonly used in real applications. We will then discuss the problem of estimating unknown parameters in state-space models. The most common statistical algorithm for estimating the parameters of a space-state model is based on EM algorithm, which makes it possible to iteratively compute a numerical approximation of the maximum likelihood estimators. We will show that the EM and CPF-BS algorithms can be combined to effectively estimate the parameters in toy models. In some applications, the dynamical model is unknown or very expensive to solve numerically but observations or simulations are available. It is thence possible to reconstruct the state conditionally to the observations by using filtering/smoothing algorithms in which the dynamical model is replaced by a statistical emulator constructed from the observations. We will show that the EM and CPF-BS algorithms can be adapted in this framework and allow to provide non-parametric estimation of the dynamic model of the state from noisy observations. Finally the proposed algorithms are applied to impute wind data (produced by Méteo France)
Čabla, Adam. "Odhady v analýze přežívání." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-17134.
Full textNakamura, Luiz Ricardo. "Advances on the Birnbaum-Saunders distribution." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-30092016-171320/.
Full textA 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.
Malsiner-Walli, Gertraud, Paul Hofmarcher, and Bettina Grün. "Semi-parametric Regression under Model Uncertainty: Economic Applications." Wiley, 2019. http://dx.doi.org/10.1111/obes.12294.
Full textTano, Richard. "Determining multimediastreaming content." Thesis, Umeå universitet, Institutionen för fysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-50376.
Full textDetta examensarbete skrevs av Richard Tano student på Umeå universitet åt Ericsson Luleå. Övervakning av nätets prestanda är av yttersta vikt för nätverksleverantörer. Detta görs med modeller för att utvärdera QoS (Quality of Service) som överensstämmer med ITU-T rekommendationer. Vid bestämning av kvaliten på videoströmmar är det mer meningsfullt att utvärdera QoE (Quality of Experience) för att få insikt i hur användaren uppfattar kvaliten. Detta graderas i värden av MOS (Mean opinion score). En viktig aspekt för att bestämma QoE är typen av videoinnehåll, vilket är korrelerat till videons kodningskomplexitet och MOS värden. I detta arbete undersöktes möjligheterna att förbättra kvalitetsuppskattningsmodellerna under uppfyllande av ITU-T studygroup 12 (q.14). Metoder undersöktes och en algoritm utvecklades som använder tidsserieanalys av paketstatistik för uppskattning av videoströmmars MOS-värden. Metoder som ingår i algoritmen är en nyutvecklad frekventa mönster metod tillsammans med regressions analys. En modell som använder algoritmen från låg till hög bithastighet definierades. Den nya modellen gav omkring 20% förbättrad precision i uppskattning av MOS-värden jämfört med existerande referensmodell. Även en algoritm som enbart använder regressionsstatistik och modellerande av statistiska parametrar utvecklades. Denna algoritm levererade jämförbara resultat med föregående algoritm men gav även kraftigt förbättrad effektivitet.
Mays, James Edward. "Model robust regression: combining parametric, nonparametric, and semiparametric methods." Diss., Virginia Polytechnic Institute and State University, 1995. http://hdl.handle.net/10919/49937.
Full textPh. D.
incomplete_metadata
Starnes, Brett Alden. "Asymptotic Results for Model Robust Regression." Diss., Virginia Tech, 1999. http://hdl.handle.net/10919/30244.
Full textPh. D.
Zhang, Tianyang. "Partly parametric generalized additive model." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/913.
Full textHoglin, Phillip J. "Survival analysis and accession optimization of prior enlisted United States Marine Corps officers." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1673.
Full textThe purpose of this thesis is to firstly analyze the determinants on the survival of United States Marine Corps Officers, and secondly, to develop the methodology to optimize the accessions of prior and non-prior enlisted officers. Using data from the Marine Corps Officer Accession Career file (MCCOAC), the Cox Proportional Hazards Model is used to estimate the effects of officer characteristics on their survival as a commissioned officer in the USMC. A Markov model for career transition is combined with fiscal data to determine the optimum number of prior and non-prior enlisted officers under the constraints of force structure and budget. The findings indicate that prior enlisted officers have a better survival rate than their non-prior enlisted counterparts. Additionally, officers who are married, commissioned through MECEP, graduate in the top third of their TBS class, and are assigned to a combat support MOS have a better survival rate than officers who are unmarried, commissioned through USNA, graduate in the middle third of their TBS class, and are assigned to either combat or combat service support MOS. The findings also indicate that the optimum number of prior enlisted officer accessions may be considerably lower than recent trends and may differ across MOS. Based on the findings; it is recommended that prior enlisted officer accession figures be reviewed.
Major, Australian Army
Valença, Dione Maria. "O modelo de regressão gama generalizada para discriminar entre modelos parametricos de tempo de vida." [s.n.], 1994. http://repositorio.unicamp.br/jspui/handle/REPOSIP/325403.
Full textDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Ciencia da Computação
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Resumo: Não informado.
Abstract: Not informed
Mestrado
Mestre em Estatística
Diniz, Márcio Augusto. "Modelos bayesianos semi-paramétricos para dados binários." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-02112015-013658/.
Full textThis work proposes semi-parametric Bayesian models for binary data. The first model is a scale mixture that allows handling discrepancies related to kurtosis of Logistic model. It is a more interesting extension than has been proposed by Basu e Mukhopadyay (1998) because this model allows the interpretation of the prior distribution of parameters using odds ratios. The second model enjoys the scale mixture together with the scale transformation proposed by Yeo and Johnson (2000) modeling the kurtosis and the asymmetry such that a parameter of asymmetry is estimated. This transformation is more appropriate to deal with negative values than the transformation of Box e Cox (1964) used by Guerrero e Johnson (1982) and simpler than the model proposed by Stukel (1988). Finally, the third model is the most general among all and consists of a location-scale mixture that can describe kurtosis and skewness also bimodality. The model proposed by Newton et al (1996), although general, does not allow a tangible interpretation of the a priori distribution for reseachers of applied area. The evaluation of the models is performed through distance measurements of distribution of probabilities Cramer-von Mises Kolmogorov-Smirnov and Anderson-Darling and also the Conditional Predictive sorted.
Hossain, Shahadut. "Dealing with measurement error in covariates with special reference to logistic regression model: a flexible parametric approach." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/408.
Full textTorrent, Hudson da Silva. "Estimação não-paramétrica e semi-paramétrica de fronteiras de produção." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2010. http://hdl.handle.net/10183/25786.
Full textThere exists a large and growing literature on the specification and estimation of production frontiers and therefore efficiency of production units. In this thesis we focus on deterministic production frontier models, which are based on the assumption that all observed data lie in the technological set. Among the existing statistical models and estimators for deterministic frontiers, a promising approach is that of Martins-Filho and Yao (2007). They propose an estimation procedure that consists of three stages. Their estimator is fairly easy to implement as it involves standard nonparametric procedures. In addition, it has a number of desirable characteristics vis-a-vis traditional deterministic frontier estimators as DEA and FDH. In this thesis we propose three papers that improve the model proposed in Martins-Filho and Yao (2007). In the first paper we improve their estimation procedure by adopting a variant of the local exponential smoothing proposed in Ziegelmann (2002). Our estimator is shown to be consistent and asymptotically normal. In addition, due to local exponential smoothing, potential negativity of conditional variance functions that may hinder the use of Martins-Filho and Yao's estimator is avoided. In the second paper we propose a novel method for estimating production frontiers in only two stages. (Continue). There we show that we can eliminate the second stage of Martins-Filho and Yao as well as of our first paper, where estimation of the same frontier model requires three stages under different versions for the second stage. We study asymptotic properties showing consistency andNirtnin, asymptotic normality of our proposed estimator under standard assumptions. In the third paper we propose a semiparametric variation of the frontier model studied in the second paper. We rewrite that model allowing for estimating the production frontier and efficiency of production units in a multiple input context without suffering the curse of dimensionality. Our approach places that model within the framework of additive models based on assumptions regarding the way inputs combine in production. In particular, we consider the cases of additive and multiplicative inputs, which are widely considered in economic theory and applications. Monte Carlo studies are performed in all papers to shed light on the finite sample properties of the proposed estimators. Furthermore a real data study is carried out in all papers, from which we rank efficiency within a sample of USA Law Enforcement agencies using USA crime data.
Hoare, Armando. "Parametric, non-parametric and statistical modeling of stony coral reef data." [Tampa, Fla] : University of South Florida, 2008. http://purl.fcla.edu/usf/dc/et/SFE0002470.
Full textRace, Jonathan Andrew. "Semi-parametric Survival Analysis via Dirichlet Process Mixtures of the First Hitting Time Model." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu157357742741077.
Full textDevamitta, Perera Muditha V. "Statistical Analysis and Modeling of Ovarian and Breast Cancer." Scholar Commons, 2017. https://scholarcommons.usf.edu/etd/7395.
Full textKnefati, Muhammad Anas. "Estimation non-paramétrique du quantile conditionnel et apprentissage semi-paramétrique : applications en assurance et actuariat." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2280/document.
Full textThe thesis consists of two parts: One part is about the estimation of conditional quantiles and the other is about supervised learning. The "conditional quantile estimate" part is organized into 3 chapters. Chapter 1 is devoted to an introduction to the local linear regression and then goes on to present the methods, the most used in the literature to estimate the smoothing parameter. Chapter 2 addresses the nonparametric estimation methods of conditional quantile and then gives numerical experiments on simulated data and real data. Chapter 3 is devoted to a new conditional quantile estimator, we propose. This estimator is based on the use of asymmetrical kernels w.r.t. x. We show, under some hypothesis, that this new estimator is more efficient than the other estimators already used. The "supervised learning" part is, too, with 3 chapters: Chapter 4 provides an introduction to statistical learning, remembering the basic concepts used in this part. Chapter 5 discusses the conventional methods of supervised classification. Chapter 6 is devoted to propose a method of transferring a semiparametric model. The performance of this method is shown by numerical experiments on morphometric data and credit-scoring data
Abdel-Salam, Abdel-Salam Gomaa. "Profile Monitoring with Fixed and Random Effects using Nonparametric and Semiparametric Methods." Diss., Virginia Tech, 2009. http://hdl.handle.net/10919/29387.
Full textPh. D.
Tran, Xuan Quang. "Les modèles de régression dynamique et leurs applications en analyse de survie et fiabilité." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0147/document.
Full textThis thesis was designed to explore the dynamic regression models, assessing the sta-tistical inference for the survival and reliability data analysis. These dynamic regressionmodels that we have been considered including the parametric proportional hazards andaccelerated failure time models contain the possibly time-dependent covariates. We dis-cussed the following problems in this thesis.At first, we presented a generalized chi-squared test statisticsY2nthat is a convenient tofit the survival and reliability data analysis in presence of three cases: complete, censoredand censored with covariates. We described in detail the theory and the mechanism to usedofY2ntest statistic in the survival and reliability data analysis. Next, we considered theflexible parametric models, evaluating the statistical significance of them by usingY2nandlog-likelihood test statistics. These parametric models include the accelerated failure time(AFT) and a proportional hazards (PH) models based on the Hypertabastic distribution.These two models are proposed to investigate the distribution of the survival and reliabilitydata in comparison with some other parametric models. The simulation studies were de-signed, to demonstrate the asymptotically normally distributed of the maximum likelihood estimators of Hypertabastic’s parameter, to validate of the asymptotically property of Y2n test statistic for Hypertabastic distribution when the right censoring probability equal 0% and 20%.n the last chapter, we applied those two parametric models above to three scenes ofthe real-life data. The first one was done the data set given by Freireich et al. on thecomparison of two treatment groups with additional information about log white blood cellcount, to test the ability of a therapy to prolong the remission times of the acute leukemiapatients. It showed that Hypertabastic AFT model is an accurate model for this dataset.The second one was done on the brain tumour study with malignant glioma patients, givenby Sauerbrei & Schumacher. It showed that the best model is Hypertabastic PH onadding five significance covariates. The third application was done on the data set given by Semenova & Bitukov on the survival times of the multiple myeloma patients. We did not propose an exactly model for this dataset. Because of that was an existing oneintersection of survival times. We, therefore, suggest fitting other dynamic model as SimpleCross-Effect model for this dataset
Mackových, Marek. "Regresní analýza EKG pro odhad polohy srdce vůči měřicím elektrodám." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220859.
Full textKozáček, Vojtěch. "Experimentální stanovení závislosti parametrů NDT a pevnosti v tlaku betonu." Master's thesis, Vysoké učení technické v Brně. Fakulta stavební, 2020. http://www.nusl.cz/ntk/nusl-409957.
Full textBršlicová, Tereza. "Bezkontaktní detekce fyziologických parametrů z obrazových sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221320.
Full textSavegnago, Rodrigo Pelicioni [UNESP]. "Modelos de regressão aleatória, análise multivariada e redes neurais artificiais na avaliação genética da produção de leite de vacas Holandesas." Universidade Estadual Paulista (UNESP), 2013. http://hdl.handle.net/11449/102800.
Full textOs objetivos deste trabalho foram estimar parâmetros genéticos para a produção de leite utilizando modelos de regressão aleatória, comparar o ganho genético esperado da produção de leite utilizando diferentes índices de seleção, utilizar análise de agrupamento e discriminante para explorar o perfil genético dos animais para a produção de leite, visando identificar os animais mais indicados para a seleção e investigar quais informações devem ser utilizadas em redes neurais artificiais para que fossem capazes de predizer os valores genéticos dos animais para produção de leite total até 305 dias em lactação. As estimativas de herdabilidade dos controles mensais da produção de leite variaram de 0,12 ± 0,04 a 0,31 ± 0,04. As estimativas de correlação genética e de ambiente permanente apresentaram valores próximos à unidade em controles leiteiros adjacentes, com tendência de diminuição das correlações à medida que o tempo entre os controles leiteiros aumentou. As magnitudes das estimativas de herdabilidade para as classes dos controles leiteiros mensais indicam que a produção de leite deve responder ao processo de seleção dos animais e a fase entre 121 a 240 dias em lactação teria melhor resposta à seleção devido as maiores estimativas de herdabilidade nesse período. A seleção dos animais baseada na produção de leite entre 121 a 150 dias em lactação é recomendada devido à alta herdabilidade para a característica nesta fase e pelas altas correlações genéticas da característica neste período com os demais da lactação. A indicação de qual índice de seleção deve ser utilizado dependerá, entre outros fatores, dos objetivos de seleção estabelecidos pelo programa de melhoramento genético. Se o objetivo de seleção for melhorar a produção de leite e a persistência da lactação, seria mais indicado utilizar índices de seleção baseado...
The objectives of this study were to estimate genetic parameters for milk yield using random regression models, to compare the expected genetic gain of this trait using different selection indexes, to use cluster and discriminant analyses to explore the genetic pattern of milk production of the animals to identify those ones most suitable for selection, and to investigate which information should be used in artificial neural networks to predict the breeding values for milk yield to 305 days in milks. The estimates of heritability for monthly milk production classes ranged from 0.12 ± 0.04 to 0.31 ± 0.04. The estimates of genetic correlation and permanent environment had values close to one in adjacent dairy controls, and decreased as the time between the controls had increased. The magnitudes of heritabilities between 121 to 240 days indicated that the milk yield could better response to selection in this phase due to the highest estimates. The selection based on milk production between 121 to 150 days in milk is recommended due to the high heritability for the trait at this phase and due to the high genetic correlations with milk yield in the other periods of the lactation. The use of a particular selection index will depend on the selection goals of the breeding program. The selection indexes based on eigenvectors of additive genetic matrix with greater selection emphasis for persistence is recommended if the selection goal of the breeding program is to improve milk production and persistence simultaneously. But, if the selection goal is to improve only the milk production, the selection index based on breeding values for milk production up to 305 days in milk would be the most appropriate, because it presented the greatest expected genetic gain for this trait. The breeding values of milk yield on every 30 days were used as grouping variables of the animals. It was found that the population ...
Savegnago, Rodrigo Pelicioni. "Modelos de regressão aleatória, análise multivariada e redes neurais artificiais na avaliação genética da produção de leite de vacas Holandesas /." Jaboticabal, 2013. http://hdl.handle.net/11449/102800.
Full textCoorientador: Lenira El Faro
Banca: João Ademir de Oliveira
Banca: Sandra Aidar de Queiroz
Banca: Cláudia Cristina Paro de Paz
Banca: José Bento Sterman Ferraz
Resumo: Os objetivos deste trabalho foram estimar parâmetros genéticos para a produção de leite utilizando modelos de regressão aleatória, comparar o ganho genético esperado da produção de leite utilizando diferentes índices de seleção, utilizar análise de agrupamento e discriminante para explorar o perfil genético dos animais para a produção de leite, visando identificar os animais mais indicados para a seleção e investigar quais informações devem ser utilizadas em redes neurais artificiais para que fossem capazes de predizer os valores genéticos dos animais para produção de leite total até 305 dias em lactação. As estimativas de herdabilidade dos controles mensais da produção de leite variaram de 0,12 ± 0,04 a 0,31 ± 0,04. As estimativas de correlação genética e de ambiente permanente apresentaram valores próximos à unidade em controles leiteiros adjacentes, com tendência de diminuição das correlações à medida que o tempo entre os controles leiteiros aumentou. As magnitudes das estimativas de herdabilidade para as classes dos controles leiteiros mensais indicam que a produção de leite deve responder ao processo de seleção dos animais e a fase entre 121 a 240 dias em lactação teria melhor resposta à seleção devido as maiores estimativas de herdabilidade nesse período. A seleção dos animais baseada na produção de leite entre 121 a 150 dias em lactação é recomendada devido à alta herdabilidade para a característica nesta fase e pelas altas correlações genéticas da característica neste período com os demais da lactação. A indicação de qual índice de seleção deve ser utilizado dependerá, entre outros fatores, dos objetivos de seleção estabelecidos pelo programa de melhoramento genético. Se o objetivo de seleção for melhorar a produção de leite e a persistência da lactação, seria mais indicado utilizar índices de seleção baseado ...
Abstract: The objectives of this study were to estimate genetic parameters for milk yield using random regression models, to compare the expected genetic gain of this trait using different selection indexes, to use cluster and discriminant analyses to explore the genetic pattern of milk production of the animals to identify those ones most suitable for selection, and to investigate which information should be used in artificial neural networks to predict the breeding values for milk yield to 305 days in milks. The estimates of heritability for monthly milk production classes ranged from 0.12 ± 0.04 to 0.31 ± 0.04. The estimates of genetic correlation and permanent environment had values close to one in adjacent dairy controls, and decreased as the time between the controls had increased. The magnitudes of heritabilities between 121 to 240 days indicated that the milk yield could better response to selection in this phase due to the highest estimates. The selection based on milk production between 121 to 150 days in milk is recommended due to the high heritability for the trait at this phase and due to the high genetic correlations with milk yield in the other periods of the lactation. The use of a particular selection index will depend on the selection goals of the breeding program. The selection indexes based on eigenvectors of additive genetic matrix with greater selection emphasis for persistence is recommended if the selection goal of the breeding program is to improve milk production and persistence simultaneously. But, if the selection goal is to improve only the milk production, the selection index based on breeding values for milk production up to 305 days in milk would be the most appropriate, because it presented the greatest expected genetic gain for this trait. The breeding values of milk yield on every 30 days were used as grouping variables of the animals. It was found that the population ...
Doutor
Bertipaglia, Tássia Souza [UNESP]. "Estimativas de parâmetros genéticos para pesos do nascimento aos dois anos de idade para bovinos da raça Brahman utilizando modelos de regressão aleatória." Universidade Estadual Paulista (UNESP), 2013. http://hdl.handle.net/11449/92572.
Full textO objetivo deste trabalho foi estimar funções de covariância utilizando modelos de regressão aleatória para a análise de medidas repetidas de pesos de bovinos Brahman do Brasil. Parâmetros genéticos foram estimados para 88.788 registros de peso do nascimento aos 744 dias de idade de 17.499 animais provenientes do banco de dados da Associação Brasileira de Criadores de Zebuínos (ABCZ). Os modelos incluíram, como aleatórios, os efeitos genéticos aditivo direto e materno, e ambiente permanente do animal, como fixo o efeito de grupo contemporâneos, e como covariável a idade da vaca ao parto (quadrática) aninhada a classe de idade do animal. As análises de regressão aleatória foram realizadas utilizando polinômio ortogonal de Legendre de quarta ordem para modelar as tendências da média populacional. As variâncias residuais foram modeladas por uma função homogênea e com cinco níveis de classes de idade. Os modelos foram comparados pelos critérios de informação bayesiano de Schwarz (BIC) e Akaike (AIC). O melhor modelo indicado pelos critérios foi o que considerou o efeito genético aditivo direto ajustado por um polinômio quadrático, o efeito genético materno por cúbico, e o efeito de ambiente permanente do animal por cúbico, e a heterogeneidade de variâncias residuais (5 níveis) . As estimativas de herdabilidade para o efeito direto foram maiores ao início e ao final do período estudado, com valores de 0,47 ao nascimento, 0,38 aos 60 e 120 dias, 0,53 aos 205 dias, 0,70 aos 365 dias, 0,76 aos 550 e 0,52 aos 744 dias de idade. As estimativas de herdabilidade materna foram máximas ao nascimento (0,16). As correlações genéticas de maneira geral, exceto para pesos ao nascimento, variaram de moderadas a altas diminuindo conforme o aumento da distância entre as idades. Maior eficiência na seleção para peso pode ser obtida considerando os pesos próximos à desmama...
The objective of this study was to estimate covariance functions using random regression models for repeated measures analysis of weights of Brahman cattle in Brazil. Genetic parameters were estimated for 88,788 records from birth to 744 days of age of 17,499 animals from the database of the Brazilian Association of Zebu Breeders (ABCZ). The models included the random additive direct genetic effects and maternal permanent environment and the animal, as the fixed effect of contemporary group and the covariate age at calving (quadratic) nested class of the animal's age. Regression analyzes were performed using random orthogonal Legendre polynomials of fourth order to model trends in population mean. The residual variances were modeled by a homogeneous function with five levels and age classes. The models were compared by the information criteria Schwarz Bayesian (BIC) and Akaike (AIC). The best model indicated by what criteria was considered the direct genetic effect adjusted by a quadratic polynomial, the maternal genetic effect for cubic and permanent environmental effect of the animal by Cubic, and heterogeneity of residual variances (5 levels). Heritability estimates for direct effect were higher at the beginning and end of the study period, with values of 0.47 at birth, 0.38 at 60 and 120 days to 205 days 0.53, 0.70 at 365 days, 0.76 and 0.52 at 550 to 744 days of age. The maternal heritability estimates were maximal at birth (0.16). Genetic correlations in general, except for birth weights ranged from moderate to high decreases as the distance increases between the ages. Efficiency of selection for weight can be obtained by considering the weights near weaning period in which the estimates of genetic variance and heritability were growing
Bertipaglia, Tássia Souza. "Estimativas de parâmetros genéticos para pesos do nascimento aos dois anos de idade para bovinos da raça Brahman utilizando modelos de regressão aleatória /." Jaboticabal, 2013. http://hdl.handle.net/11449/92572.
Full textBanca: Danísio Prado Munari
Banca: Maria Eugênia Zerlotti Mercadante
Resumo: O objetivo deste trabalho foi estimar funções de covariância utilizando modelos de regressão aleatória para a análise de medidas repetidas de pesos de bovinos Brahman do Brasil. Parâmetros genéticos foram estimados para 88.788 registros de peso do nascimento aos 744 dias de idade de 17.499 animais provenientes do banco de dados da Associação Brasileira de Criadores de Zebuínos (ABCZ). Os modelos incluíram, como aleatórios, os efeitos genéticos aditivo direto e materno, e ambiente permanente do animal, como fixo o efeito de grupo contemporâneos, e como covariável a idade da vaca ao parto (quadrática) aninhada a classe de idade do animal. As análises de regressão aleatória foram realizadas utilizando polinômio ortogonal de Legendre de quarta ordem para modelar as tendências da média populacional. As variâncias residuais foram modeladas por uma função homogênea e com cinco níveis de classes de idade. Os modelos foram comparados pelos critérios de informação bayesiano de Schwarz (BIC) e Akaike (AIC). O melhor modelo indicado pelos critérios foi o que considerou o efeito genético aditivo direto ajustado por um polinômio quadrático, o efeito genético materno por cúbico, e o efeito de ambiente permanente do animal por cúbico, e a heterogeneidade de variâncias residuais (5 níveis) . As estimativas de herdabilidade para o efeito direto foram maiores ao início e ao final do período estudado, com valores de 0,47 ao nascimento, 0,38 aos 60 e 120 dias, 0,53 aos 205 dias, 0,70 aos 365 dias, 0,76 aos 550 e 0,52 aos 744 dias de idade. As estimativas de herdabilidade materna foram máximas ao nascimento (0,16). As correlações genéticas de maneira geral, exceto para pesos ao nascimento, variaram de moderadas a altas diminuindo conforme o aumento da distância entre as idades. Maior eficiência na seleção para peso pode ser obtida considerando os pesos próximos à desmama ...
Abstract: The objective of this study was to estimate covariance functions using random regression models for repeated measures analysis of weights of Brahman cattle in Brazil. Genetic parameters were estimated for 88,788 records from birth to 744 days of age of 17,499 animals from the database of the Brazilian Association of Zebu Breeders (ABCZ). The models included the random additive direct genetic effects and maternal permanent environment and the animal, as the fixed effect of contemporary group and the covariate age at calving (quadratic) nested class of the animal's age. Regression analyzes were performed using random orthogonal Legendre polynomials of fourth order to model trends in population mean. The residual variances were modeled by a homogeneous function with five levels and age classes. The models were compared by the information criteria Schwarz Bayesian (BIC) and Akaike (AIC). The best model indicated by what criteria was considered the direct genetic effect adjusted by a quadratic polynomial, the maternal genetic effect for cubic and permanent environmental effect of the animal by Cubic, and heterogeneity of residual variances (5 levels). Heritability estimates for direct effect were higher at the beginning and end of the study period, with values of 0.47 at birth, 0.38 at 60 and 120 days to 205 days 0.53, 0.70 at 365 days, 0.76 and 0.52 at 550 to 744 days of age. The maternal heritability estimates were maximal at birth (0.16). Genetic correlations in general, except for birth weights ranged from moderate to high decreases as the distance increases between the ages. Efficiency of selection for weight can be obtained by considering the weights near weaning period in which the estimates of genetic variance and heritability were growing
Mestre
Balzotti, Christopher Stephen. "Multidisciplinary Assessment and Documentation of Past and Present Human Impacts on the Neotropical Forests of Petén, Guatemala." BYU ScholarsArchive, 2010. https://scholarsarchive.byu.edu/etd/2129.
Full textNováková, Marie. "Mapování pohybových artefaktů ve fMRI." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220039.
Full textBhatti, Sajjad Haider. "Estimation of the mincerian wage model addressing its specification and different econometric issues." Phd thesis, Université de Bourgogne, 2012. http://tel.archives-ouvertes.fr/tel-00780563.
Full textCardozo, Sandra Vergara. "Função da probabilidade da seleção do recurso (RSPF) na seleção de habitat usando modelos de escolha discreta." Universidade de São Paulo, 2009. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-11032009-143806/.
Full textIn ecology, the behavior of animals is often studied to better understand their preferences for different types of habitat and food. The present work is concerned with this topic. It is divided into three chapters. The first concerns the estimation of a resource selection probability function (RSPF) compared with a discrete choice model (DCM) using chi-squared to obtain estimates. The best estimates were obtained by the DCM method. Nevertheless, animals were not selected based on choice alone. With RSPF, the maximum likelihood estimates used with the logistic regression still did not reach the objectives, since the animals have more than one choice. R and Minitab software and the FORTRAN programming language were used for the computations in this chapter. The second chapter discusses further the likelihood presented in the first chapter. A new likelihood for a RSPF is presented, which takes into account the units used and not used, and parametric and non-parametric bootstrapping are employed to study the bias and variance of parameter estimators, using a FORTRAN program for the calculations. In the third chapter, the new likelihood presented in chapter 2, with a discrete choice model is used to resolve a part of the problem presented in the first chapter. A nested structure is proposed for modelling selection by 28 spotted owls (Strix occidentalis) as well as a generalized nested logit model using random utility maximization and a random RSPF. Numerical optimization methods and the SAS system were employed to estimate the nested structural parameters.
Silva, Kesley Leandro da. "Estratégias de momentum no mercado cambial." reponame:Repositório Institucional do FGV, 2016. http://hdl.handle.net/10438/15773.
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Utilizo dados semanais para investigar a lucratividade de estratégias de momentum no mercado de câmbio baseadas em dois diferentes métodos de extração da tendência, possivelmente não linear. Comparo a performance com as tradicionais regras de médias móveis, método linear bastante utilizado pelos profissionais do mercado. Eu encontro que o desempenho de todas as estratégias é extremamente sensível à escolha da moeda, às defasagens utilizadas e ao critério de avaliação escolhido. A despeito disso, as moedas dos países do G10 apresentam resultados médios melhores com a utilização dos métodos não lineares, enquanto as moedas dos países emergentes apresentam resultados mistos. Adoto também uma metodologia para o gerenciamento do risco das estratégias de momentum, visando minimizar as 'grandes perdas'. Ela tem êxito em diminuir as perdas máximas semanais, o desvio-padrão, a assimetria e curtose para a maior parte das moedas em ambas as estratégias. Quanto ao desempenho, as operações baseadas no filtro HP com gestão do risco apresentam retornos e índices de Sharpe maiores para cerca de 70% das estratégias, enquanto as baseadas na regressão não paramétrica apresentam resultados melhores para cerca de 60% das estratégias.
I use weekly data to investigate the profitability of momentum strategies in the currency market based on two different methods of trending extraction, possibly nonlinear. I compare the performance with the traditional moving averages rules, linear method of trading broadly used by market professionals. I find that the performance of all strategies is extremely sensitive to the choice of currency, lags parameters and the evaluation criteria. Nevertheless, the G10 currencies show better average results with the nonlinear methods, while the emerging market currencies show mixed results. I also adopt a methodology for managing the risk of momentum strategies to minimize the “worst crashes”. It works to lower the maximum weekly losses, the standard deviation, the skewness and the kurtosis for most currencies in both strategies. In terms of performance, HP filter with risk-managed momentum shows higher return and Sharpe ratio for about 70% the observations, while those based on nonparametric regression show higher numbers for about 60% the observations.
Matias, Stephane Paul Jordão. "Análise paramétrica do consumo de electricidade e água para o comércio alimentar a retalho e grossista." Master's thesis, Instituto Superior de Economia e Gestão, 2012. http://hdl.handle.net/10400.5/10327.
Full textOs consumos de electricidade e água têm sido alvo de vários estudos, com o interesse de perceber o que os influencia e encontrar soluções que promovam a melhoria do desempenho económico e ambiental das organizações. Neste sentido, o presente trabalho pretende elaborar uma análise paramétrica, utilizando o modelo de regressão linear, para detectar as variáveis das quais dependem os consumos de água e electricidade para os formatos de comércio a retalho e grossista. Esta análise permitiu estudar a relevância de algumas variáveis para explicar os respectivos consumos nos vários estabelecimentos do grupo Jerónimo Martins como também detectar os estabelecimentos com consumos extremos.
The electricity and water consumptions has been the subject of several studies which envisage the evaluation of their influences and finding solutions that promote the improvement of the organizations' economic and environmental performance. In this sense, the present work aims to develop a parametric analysis, using the linear regression model, to detect the variables of which depend the consumptions of water and electricity for the retail and cash & carry sectors. This analysis allowed to study the relevance of some variables in explaining the mentioned consumptions of various establishments of the group Jerónimo Martins and to detect the establishments with extreme consumptions.
Kamari, Halaleh. "Qualité prédictive des méta-modèles construits sur des espaces de Hilbert à noyau auto-reproduisant et analyse de sensibilité des modèles complexes." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASE010.
Full textIn this work, the problem of estimating a meta-model of a complex model, denoted m, is considered. The model m depends on d input variables X1 , ..., Xd that are independent and have a known law. The meta-model, denoted f ∗ , approximates the Hoeffding decomposition of m, and allows to estimate its Sobol indices. It belongs to a reproducing kernel Hilbert space (RKHS), denoted H, which is constructed as a direct sum of Hilbert spaces (Durrande et al. (2013)). The estimator of the meta-model, denoted f^, is calculated by minimizing a least-squares criterion penalized by the sum of the Hilbert norm and the empirical L2-norm (Huet and Taupin (2017)). This procedure, called RKHS ridge group sparse, allows both to select and estimate the terms in the Hoeffding decomposition, and therefore, to select the Sobol indices that are non-zero and estimate them. It makes possible to estimate the Sobol indices even of high order, a point known to be difficult in practice.This work consists of a theoretical part and a practical part. In the theoretical part, I established upper bounds of the empirical L2 risk and the L2 risk of the estimator f^. That is, upper bounds with respect to the L2-norm and the empirical L2-norm for the f^ distance between the model m and its estimation f into the RKHS H. In the practical part, I developed an R package, called RKHSMetaMod, that implements the RKHS ridge group sparse procedure and a spacial case of it called the RKHS group lasso procedure. This package can be applied to a known model that is calculable in all points or an unknown regression model. In order to optimize the execution time and the storage memory, except for a function that is written in R, all of the functions of the RKHSMetaMod package are written using C++ libraries GSL and Eigen. These functions are then interfaced with the R environment in order to propose an user friendly package. The performance of the package functions in terms of the predictive quality of the estimator and the estimation of the Sobol indices, is validated by a simulation study
Liley, Albert James. "Statistical co-analysis of high-dimensional association studies." Thesis, University of Cambridge, 2017. https://www.repository.cam.ac.uk/handle/1810/270628.
Full textWinkler, Anderson M. "Widening the applicability of permutation inference." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:ce166876-0aa3-449e-8496-f28bf189960c.
Full textAhmed, Mohamed Salem. "Contribution à la statistique spatiale et l'analyse de données fonctionnelles." Thesis, Lille 3, 2017. http://www.theses.fr/2017LIL30047/document.
Full textThis thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country