SEMIPARAMETRIC METHODS IN THE ESTIMATION OF TAIL PROBABILITIES AND EXTREME QUANTILES

In quantitative risk analysis, the problem of estimating small threshold exceedance probabilities and extreme quantiles arise ubiquitously in bio-surveillance, economics, natural disaster insurance actuary, quality control schemes, etc. A useful way to make an assessment of extreme events is to estimate the probabilities of exceeding large threshold values and extreme quantiles judged by interested authorities. Such information regarding extremes serves as essential guidance to interested authorities in decision making processes. However, in such a context, data are usually skewed in nature, and the rarity of exceedance of large threshold implies large fluctuations in the distribution's upper tail, precisely where the accuracy is desired mostly. Extreme Value Theory (EVT) is a branch of statistics that characterizes the behavior of upper or lower tails of probability distributions. However, existing methods in EVT for the estimation of small threshold exceedance probabilities and extreme quantiles often lead to poor predictive performance in cases where the underlying sample is not large enough or does not contain values in the distribution's tail. In this dissertation, we shall be concerned with an out of sample semiparametric (SP) method for the estimation of small threshold probabilities and extreme quantiles. The proposed SP method for interval estimation calls for the fusion or integration of a given data sample with external computer generated independent samples. Since more data are used, real as well as artificial, under certain conditions the method produces relatively short yet reliable confidence intervals for small exceedance probabilities and extreme quantiles.

INTERACTIVE EFFECT OF FIXED TIME ORDER STRATEGIES WITHIN A SUPPLY CHAIN ON ACTUAL IN-STOCK PROBABILITIES

This report mainly deals with the interactive effect of different in-stock probabilities used by every individual in a supply chain. Based on a simulation for 10,000 weeks, the effects of varying in-stock probabilities are observed. Based on these observations, an individual in a supply chain can take counter measures in order to avoid stock out chances hence maintaining profits.

Factors affecting southern water vole (Arvicola sapidus) detection and occupancy probabilities in Mediterranean farmland

Failure to detect a species at sites where it is present (i.e. imperfect detection) is known to occur frequently, but this is often disregarded in monitoring programs and metapopulation studies. Here we modelled for the first time the probability of patch occupancy by a threatened small mammal, the southern water vole (Arvicola sapidus, while accounting for the probability of detection given occupancy. Based on replicated presence sign surveys conducted in autumn (November–December 2013) and winter (February–March 2014) in a farmland landscape, we used occupancy detection modelling to test the effects of vegetation, sampling effort, observer experience, and rainfall on detection probability. We then assessed whether occupancy was related to patch size, isolation, vegetation, or presence of water, after correcting for imperfect detection. The mean detection probabilities of water vole signs in autumn (0.71) and winter (0.81) indicated that false absences may be generated in about 20–30% of occupied patches surveyed by a single observer on a single occasion. There was no statistical support for the effects of covariates on detectability. After controlling for imperfect detection, the mean probabilities of occupancy in autumn (0.31) and winter (0.29) were positively related to patch size and presence of water, and negatively so, albeit weakly, to patch isolation. Overall, our study underlined the importance of accounting for imperfect detection in sign surveys of small mammals such as water voles, pointing out the need to use occupancy detection modelling together with replicate surveys for accurately estimating occupancy and the factors affecting it.

Development And Characterization Of Microsatellite Loci For Ocotea Species (lauraceae) Threatened With Extinction.

The Atlantic rainforest species Ocotea catharinensis, Ocotea odorifera, and Ocotea porosa have been extensively harvested in the past for timber and oil extraction and are currently listed as threatened due to overexploitation. To investigate the genetic diversity and population structure of these species, we developed 8 polymorphic microsatellite markers for O. odorifera from an enriched microsatellite library by using 2 dinucleotide repeats. The microsatellite markers were tested for cross-amplification in O. catharinensis and O. porosa. The average number of alleles per locus was 10.2, considering all loci over 2 populations of O. odorifera. Observed and expected heterozygosities for O. odorifera ranged from 0.39 to 0.93 and 0.41 to 0.92 across populations, respectively. Cross-amplification of all loci was successfully observed in O. catharinensis and O. porosa except 1 locus that was found to lack polymorphism in O. porosa. Combined probabilities of identity in the studied Ocotea species were very low ranging from 1.0 x 10-24 to 7.7 x 10-24. The probability of exclusion over all loci estimated for O. odorifera indicated a 99.9% chance of correctly excluding a random nonparent individual. The microsatellite markers described in this study have high information content and will be useful for further investigations on genetic diversity within these species and for subsequent conservation purposes.

Rapidly Switching Multidirectional Defibrillation: Reversal Of Ventricular Fibrillation With Lower Energy Shocks.

Cardiac arrest after open surgery has an incidence of approximately 3%, of which more than 50% of the cases are due to ventricular fibrillation. Electrical defibrillation is the most effective therapy for terminating cardiac arrhythmias associated with unstable hemodynamics. The excitation threshold of myocardial microstructures is lower when external electrical fields are applied in the longitudinal direction with respect to the major axis of cells. However, in the heart, cell bundles are disposed in several directions. Improved myocardial excitation and defibrillation have been achieved by applying shocks in multiple directions via intracardiac leads, but the results are controversial when the electrodes are not located within the cardiac chambers. This study was designed to test whether rapidly switching shock delivery in 3 directions could increase the efficiency of direct defibrillation. A multidirectional defibrillator and paddles bearing 3 electrodes each were developed and used in vivo for the reversal of electrically induced ventricular fibrillation in an anesthetized open-chest swine model. Direct defibrillation was performed by unidirectional and multidirectional shocks applied in an alternating fashion. Survival analysis was used to estimate the relationship between the probability of defibrillation and the shock energy. Compared with shock delivery in a single direction in the same animal population, the shock energy required for multidirectional defibrillation was 20% to 30% lower (P < .05) within a wide range of success probabilities. Rapidly switching multidirectional shock delivery required lower shock energy for ventricular fibrillation termination and may be a safer alternative for restoring cardiac sinus rhythm.

Augmented Mixed Models For Clustered Proportion Data.

Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.

Análise bayesiana do modelo de herança monogênica no melhoramento vegetal: um exemplo com abobrinha

A common breeding strategy is to carry out basic studies to investigate the hypothesis of a single gene controlling the trait (major gene) with or without polygenes of minor effect. In this study we used Bayesian inference to fit genetic additive-dominance models of inheritance to plant breeding experiments with multiple generations. Normal densities with different means, according to the major gene genotype, were considered in a linear model in which the design matrix of the genetic effects had unknown coefficients (which were estimated in individual basis). An actual data set from an inheritance study of partenocarpy in zucchini (Cucurbita pepo L.) was used for illustration. Model fitting included posterior probabilities for all individual genotypes. Analysis agrees with results in the literature but this approach was far more efficient than previous alternatives assuming that design matrix was known for the generations. Partenocarpy in zucchini is controlled by a major gene with important additive effect and partial dominance.

Conteudos culturais do lazer : presença e aplicabilidade na hotelaria

Universidade Estadual de Campinas . Faculdade de Educação Física

Utilização da discriminação grafica de Fisher para indicação dos dermatoglifos como referencial de potencialidade de atletas de basquetebol

Universidade Estadual de Campinas. Faculdade de Educação Física

Inserção de graduados em fonoaudiologia de uma universidade pública em programas de pós-graduação

OBJETIVO: caracterizar a inserção de egressos do Curso de Fonoaudiologia da Universidade Estadual Paulista (UNESP) - Marília, em Programas de Pós-Graduação (PPG) Stricto Sensu brasileiros. MÉTODO: foram utilizadas listas de graduados e Curriculum Vitae do egresso e do orientador. RESULTADOS: dos 537 formados, 16,57% cursaram/estavam cursando PPG e destes, 98,88% em mestrado e 37,08% também em doutorado. Na grande área de conhecimento, 50% dos egressos de mestrado vincularam-se predominantemente a programas em Ciências da Saúde, 31,80% em Ciências Humanas e 13,64% em Linguística, Letras e Artes. No doutorado, 33, 33% em Ciências Humanas, 30,30% em Ciências da Saúde e em Linguística, Letras e Artes. Quanto à área de conhecimento, predominou a vinculação, no mestrado, de 30,68% em Fonoaudiologia, 28,41% em Educação, 13,64% em Linguística e 9,09% em Medicina I; e, no doutorado, de 33,33% em Educação, 30,30% em Linguística e 9,09% em Fonoaudiologia; 55,68% dissertações e 51,52% teses focalizaram a linguagem. A UNESP predominou com 39,77% no mestrado e 48,48% no doutorado. Predominou a vinculação a Programas com conceito 4 para 52,27% dos egressos do mestrado e 45,45% do doutorado. Quando constou a informação (55,68%), todos receberam fomento. O Teste de Razão de Verossimilhança não indicou diferenças significativas dos percentuais obtidos entre o mestrado e o doutorado. CONCLUSÃO: os resultados superaram os apresentados para o mesmo Estado, mostraram a característica interdisciplinar da Ciência Fonoaudiológica e o predomínio de temática em linguagem.

PENCALC: a program for penetrance estimation in autosomal dominant diseases

We present a computer program developed for estimating penetrance rates in autosomal dominant diseases by means of family kinship and phenotype information contained within the pedigrees. The program also determines the exact 95% credibility interval for the penetrance estimate. Both executable (PenCalc for Windows) and web versions (PenCalcWeb) of the software are available. The web version enables further calculations, such as heterozygosity probabilities and assessment of offspring risks for all individuals in the pedigrees. Both programs can be accessed and down-loaded freely at the home-page address http://www.ib.usp.br/~otto/software.htm.

Troponina cardíaca T para estratificação de risco na insuficiência cardíaca crônica descompensada

FUNDAMENTO: As troponinas cardíacas são marcadores altamente sensíveis e específicos de lesão miocárdica. Esses marcadores foram detectados na insuficiência cardíaca (IC) e estão associadas com mau prognóstico. OBJETIVO: Avaliar a relação da troponina T (cTnT) e suas faixas de valores com o prognóstico na IC descompensada. MÉTODOS: Estudaram-se 70 pacientes com piora da IC crônica que necessitaram de hospitalização. Na admissão, o modelo de Cox foi utilizado para avaliar as variáveis capazes de predizer o desfecho composto por morte ou re-hospitalização em razão de piora da IC durante um ano. RESULTADOS: Durante o seguimento, ocorreram 44 mortes, 36 re-hospitalizações por IC e 56 desfechos compostos. Na análise multivariada, os preditores de eventos clínicos foram: cTnT (cTnT > 0,100 ng/ml; hazard ratio (HR) 3,95 intervalo de confiança (IC) 95%: 1,64-9,49, p = 0,002), diâmetro diastólico final do ventrículo esquerdo (DDVE >70 mm; HR 1,92, IC95%: 1,06-3,47, p = 0,031) e sódio sérico (Na <135 mEq/l; HR 1,79, IC95%: 1,02-3,15, p = 0,044). Para avaliar a relação entre a elevação da cTnT e o prognóstico na IC descompensada, os pacientes foram estratificados em três grupos: cTnT-baixo (cTnT < 0,020 ng/ml, n = 22), cTnT-intermediário (cTnT > 0,020 e < 0,100 ng/ml, n = 36) e cTnT-alto (cTnT > 0,100 ng/ml, n = 12). As probabilidades de sobrevida e sobrevida livre de eventos foram: 54,2%, 31,5%, 16,7% (p = 0,020), e 36,4%, 11,5%, 8,3% (p = 0,005), respectivamente. CONCLUSÃO: A elevação da cTnT está associada com mau prognóstico na IC descompensada, e o grau dessa elevação pode facilitar a estratificação de risco

The full Bayesian significance test for mixture models: results in gene expression clustering

Gene clustering is a useful exploratory technique to group together genes with similar expression levels under distinct cell cycle phases or distinct conditions. It helps the biologist to identify potentially meaningful relationships between genes. In this study, we propose a clustering method based on multivariate normal mixture models, where the number of clusters is predicted via sequential hypothesis tests: at each step, the method considers a mixture model of m components (m = 2 in the first step) and tests if in fact it should be m - 1. If the hypothesis is rejected, m is increased and a new test is carried out. The method continues (increasing m) until the hypothesis is accepted. The theoretical core of the method is the full Bayesian significance test, an intuitive Bayesian approach, which needs no model complexity penalization nor positive probabilities for sharp hypotheses. Numerical experiments were based on a cDNA microarray dataset consisting of expression levels of 205 genes belonging to four functional categories, for 10 distinct strains of Saccharomyces cerevisiae. To analyze the method's sensitivity to data dimension, we performed principal components analysis on the original dataset and predicted the number of classes using 2 to 10 principal components. Compared to Mclust (model-based clustering), our method shows more consistent results.

The Effect of Spatial Scale on Predicting Time Series: A Study on Epidemiological System Identification

A susceptible-infective-recovered (SIR) epidemiological model based on probabilistic cellular automaton (PCA) is employed for simulating the temporal evolution of the registered cases of chickenpox in Arizona, USA, between 1994 and 2004. At each time step, every individual is in one of the states S, I, or R. The parameters of this model are the probabilities of each individual (each cell forming the PCA lattice ) passing from a state to another state. Here, the values of these probabilities are identified by using a genetic algorithm. If nonrealistic values are allowed to the parameters, the predictions present better agreement with the historical series than if they are forced to present realistic values. A discussion about how the size of the PCA lattice affects the quality of the model predictions is presented. Copyright (C) 2009 L. H. A. Monteiro et al.