oalib
Search Results: 1 - 10 of 100 matches for " "
All listed articles are free for downloading (OA Articles)
Page 1 /100
Display every page Item
Phenotypic stability via ammi model with bootstrap re-sampling Estabilidade fenotípica através da reamostragem “bootstrap” no modelo AMMI
Osmir José Lavoranti,Carlos Tadeu dos Santos Dias,Wojtek J. Kraznowski
Pesquisa Florestal Brasileira , 2010, DOI: 10.4336/2012.pfb.54.45
Abstract: Reliable evaluation of the stability of genotypes and environment is of prime concern to plant breeders, but the lack of a comprehensive analysis of the structure of the GE interaction has been a stumbling block to the recommendation of varieties. The Additive Main Effects and Multiplicative Interaction (AMMI) Model currently offers the good approach to interpretation and understanding of the GE interaction but lacks a way of assessing the stability of its estimates. The present contribution proposes the use of bootstrap resampling in the AMMI Model, and applies it to obtain both a graphical and a numerical analysis of the phenotypic stability of 20 Eucalyptus grandis progenies from Australia that were planted in seven environments in the Southern and Southeastern regions of Brazil. The results showed distinct behaviors of genotypes and environments and the genotype x environment interaction was significant (p value < 0.01). The bootstrap coefficient of stability based on the squared Mahalanobis distance of the scores showed that genotypes and environments can be differentiated in terms of their stabilities. Graphical analysis of the AMMI biplot provided a better understanding of the interpretation of phenotypic stability. The proposed AMMI bootstrap eliminated the uncertainties regarding the identification of low scores in traditional analyses. As posi es críticas dos estatísticos, que atuam em programas de melhoramento genético, referem-se à falta de uma análise criteriosa da estrutura da intera o do genótipo com o ambiente (GE) como um dos principais problemas para a recomenda o de cultivares. A metodologia AMMI (additive main effects and multiplicative interaction analysis) prop e ser mais eficiente que as análises usuais na interpreta o e compreens o da intera o GE, entretanto, à dificuldade de se interpretar a intera o quando há baixa explica o do primeiro componente principal; à dificuldade de se quantificar os escores como baixos, considerando estável os genótipos e/ou ambientes, além de n o apresentar o padr o de resposta do genótipo, o que caracteriza os padr es de adaptabilidade, mostram-se como os principais pontos negativos. Visando minimizar esses problemas desenvolveu-se uma metodologia via reamostragem "bootstrap", no modelo AMMI. Foram analisadas 20 progênies de Eucalyptus grandis, procedentes da Austrália, e implantadas em sete testes de progênies nas regi es Sul e Sudeste do Brasil, sendo a intera o GE significativa (valor p<0,001). A metodologia "bootstrap" AMMI eliminou as dúvidas relacionadas e mostrou-se precisa e confiáv
Sample size to estimate the Pearson correlation coefficient among characters of castor bean Tamanho de amostra para a estima o do coeficiente de correla o linear de Pearson entre caracteres de mamoneira  [cached]
Alberto Cargnelutti Filho,Sidinei José Lopes,Betania Brum,Marcos Toebe
Semina : Ciências Agrárias , 2012,
Abstract: In the study of linear relationships, it is important to define correctly the sample size, to estimate the Pearson correlation coefficient among pairs of characters, with acceptable reliability. The aim of this research was to determine the sample size (number of plants) to estimate the Pearson correlation coefficient among 21 characters of castor bean. It was evaluated 41 and 55 plants of the Sara and Lyra hybrids, respectively, regarding to the characters of seed, seedling, adult plant and yield in Santa Maria, Rio Grande do Sul State, Brazil, in the agriculture year of 2007/2008. It was calculated the sample size through bootstrap resampling, to estimate the Pearson correlation coefficient, for the amplitudes of the bootstrap confidence interval 95% equal to 0.20, 0.35 and 0.50, for each of 210 pairs of characters. Regardless of the castor bean hybrids, 96 plants are sufficient to estimate the Pearson correlation coefficient, to the amplitude of the bootstrap confidence interval 95%, maximum of the 0.52. No estudo de rela es lineares é importante dimensionar adequadamente a amostra para a estima o do coeficiente de correla o linear de Pearson entre pares de caracteres, com confiabilidade aceitável. O objetivo deste trabalho foi determinar o tamanho de amostra (número de plantas) para a estima o do coeficiente de correla o linear de Pearson entre 21 caracteres de mamoneira. Foram avaliadas 41 e 55 plantas dos híbridos Sara e Lyra, respectivamente, quanto aos caracteres de semente, de plantula, de planta adulta e de produ o, em Santa Maria, Estado do Rio Grande do Sul, no ano agrícola de 2007/2008. Calculou-se o tamanho de amostra por meio de reamostragem “bootstrap”, para a estima o do coeficiente de correla o linear de Pearson, para as amplitudes do intervalo de confian a de “bootstrap” de 95% iguais a 0,20, 0,35 e 0,50, para cada um dos 210 pares de caracteres. Independentemente do híbrido de mamoneira, 96 plantas s o suficientes para a estima o do coeficiente de correla o linear de Pearson, para a amplitude do intervalo de confian a de “bootstrap” de 95%, máxima de 0,52.
METHODS OF ESTIMATION IN MULTIPLE LINEAR REGRESSION: APPLICATION TO CLINICAL DATA MéTODOS DE ESTIMACIóN EN REGRESIóN LINEAL MúLTIPLE: APLICACIóN A DATOS CLíNICOS MéTODOS DE ESTIMA O EM REGRESS O LINEAR MúLTIPLA: APLICA O A DADOS CLíNICOS
Coelho-Barros Emílio Augusto,Sim?es Priscila Angelotti,Achcar Jorge Alberto,Martinez Edson Zangiacomi
Revista Colombiana de Estadística , 2008,
Abstract: In this paper, we show different parameters estimation forms for multiple linear regression model. We used clinical data, where the interest was to verify the relationship among the mechanical assay maximum stress with femoral mass, femoral diameter and group of ovariectomized Wistar rats. We used three inference methods: Classic inference, based on the least square method; bayesian inference, based on the Bayes theorem; and bootstrap inference, based on resampling processes. En este trabajo se muestran diferentes formas de estimación de parámetros para el modelo de regresión lineal múltiple. Para estimar los parámetros del modelo se utilizaron los datos de un ensayo clínico donde el interés era verificar la relación entre la tensión mecánica máxima del fémur con la masa y el diámetro femoral en un grupo experimental de ratas ovariectomizadas de la raza Rattus norvegicus albinos, variedad Wistar. Para estimar los parámetros del modelo se compararon tres métodos: la metodología clásica basada en el método de mínimos cuadrados, la metodología bayesiana, basada en el teorema de Bayes y la inferencia bootstrap basada en los procesos de remuestreo. Nesse artigo, tem-se o interesse em avaliar diferentes estratégias de estima o de parametros para um modelo de regress o linear múltipla. Para a estima o dos parametros do modelo foram utilizados dados de um ensaio clínico em que o interesse foi verificar se o ensaio mecanico da propriedade de for a máxima (EM-FM) está associada com a massa femoral, com o diametro femoral e com o grupo experimental de ratas ovariectomizadas da ra a Rattus norvegicus albinus, variedade Wistar. Para a estima o dos parametros do modelo ser o comparadas três metodologias: a metodologia clássica, baseada no método dos mínimos quadrados; a metodologia Bayesiana, baseada no teorema de Bayes; e o método Bootstrap, baseado em processos de reamostragem.
Avalia??o de métodos de estima??o intervalar para fun??es lineares binomiais via bootstrap infinito
Cirillo, Marcelo Angelo;Ferreira, Daniel Furtado;Safádi, Thelma;
Ciência e Agrotecnologia , 2009, DOI: 10.1590/S1413-70542009000700007
Abstract: this work aimed to evaluate confidence intervals of different linear functions of binomial proportions base on wald and wald's adjusted method using the infinity bootstrap technique. several sample sizes (ni), binomial parameters (p) and number of coefficients of the linear functions were considered. one concluded through the probabilities of binomial population confidence covered intervals that the bootstrap generalization of adjusted wald's method was efficient for linear functions whose coefficients indicated a comparison of versus the others proportion (p=0.2) considering different sample sizes.
Tamanho de amostra para a estima??o da média de dura??o dos períodos larval, pupal e larval mais pupal de Microtheca spp. por bootstrap
Cargnelutti Filho, Alberto;Toebe, Marcos;Sturza, Vinícius Soares;Bolzan, Anderson;Silveira, Tatiani Reis da;Dequech, S?nia Thereza Bastos;
Ciência Rural , 2012, DOI: 10.1590/S0103-84782012000100003
Abstract: the objective of this research was to determine the sample size (number of insects) to estimate the average duration for larval, pupal and larval + pupal periods for m. ochroloma and m. semilaevis. adults of both species were collected at a 0.25ha experimental area of forage turnip (raphanus sativus l.) and for laboratory rearing (temperature 25±2°c, relative humidity 60±10% and photoperíod 12 hours). afterwards larval, pupal and larval + pupal periods were measured, in days, of 119 and 81 insects, respectively, m. ochroloma and m. semilaevis. it was calculated measures of central tendency and variability and determined the sample size using bootstrap with replacement of 10000 samples. for estimating the average larval, pupal and larval + pupal periods, with amplitude of bootstrap confidence interval of 95%, equal a day, 42 and 35 insects are sufficient, respectively for both m. ochroloma and m. semilaevis.
The pigeonhole bootstrap  [PDF]
Art B. Owen
Statistics , 2007, DOI: 10.1214/07-AOAS122
Abstract: Recently there has been much interest in data that, in statistical language, may be described as having a large crossed and severely unbalanced random effects structure. Such data sets arise for recommender engines and information retrieval problems. Many large bipartite weighted graphs have this structure too. We would like to assess the stability of algorithms fit to such data. Even for linear statistics, a naive form of bootstrap sampling can be seriously misleading and McCullagh [Bernoulli 6 (2000) 285--301] has shown that no bootstrap method is exact. We show that an alternative bootstrap separately resampling rows and columns of the data matrix satisfies a mean consistency property even in heteroscedastic crossed unbalanced random effects models. This alternative does not require the user to fit a crossed random effects model to the data.
The Splice Bootstrap  [PDF]
Gerard Keogh
Statistics , 2013,
Abstract: This paper proposes a new bootstrap method to compute predictive intervals for nonlinear autoregressive time series model forecast. This method we call the splice boobstrap as it involves splicing the last p values of a given series to a suitably simulated series. This ensures that each simulated series will have the same set of p time series values in common, a necessary requirement for computing conditional predictive intervals. Using simulation studies we show the methods gives 90% intervals intervals that are similar to those expected from theory for simple linear and SETAR model driven by normal and non-normal noise. Furthermore, we apply the method to some economic data and demonstrate the intervals compare favourably with cross-validation based intervals.
Cluster Probability in Bootstrap Percolation  [PDF]
A. B. Harris,Andrea J. Liu
Physics , 2006,
Abstract: We develop a recursive formula for the probability of a k-cluster in bootstrap percolation.
Generalized bootstrap for estimating equations  [PDF]
Snigdhansu Chatterjee,Arup Bose
Mathematics , 2005, DOI: 10.1214/009053604000000904
Abstract: We introduce a generalized bootstrap technique for estimators obtained by solving estimating equations. Some special cases of this generalized bootstrap are the classical bootstrap of Efron, the delete-d jackknife and variations of the Bayesian bootstrap. The use of the proposed technique is discussed in some examples. Distributional consistency of the method is established and an asymptotic representation of the resampling variance estimator is obtained.
Analytical bootstrap methods for censored data  [PDF]
Alan D. Hutson
Advances in Decision Sciences , 2002, DOI: 10.1155/s1173912602000081
Abstract: Analytic bootstrap estimators for the moments of survival quantities are derived. By using these expressions recommendations can be made as to the appropriateness of bootstrap estimation under censored data conditions.
Page 1 /100
Display every page Item


Home
Copyright © 2008-2017 Open Access Library. All rights reserved.