[PDF] Top 20 A Penalized-Likelihood Method to Estimate the Distribution of Selection Coefficients from Phylogenetic Data
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A Penalized-Likelihood Method to Estimate the Distribution of Selection Coefficients from Phylogenetic Data
... large data sets (Yang and Nielsen 2008; Rodrigue et ...to estimate the evolutionary dis- tance in pairwise sequence ...the likelihood of the model could be calculated on a phylogeny to ... See full document
15
Estimating the Distribution of Selection Coefficients from Phylogenetic Data Using Sitewise Mutation-Selection Models
... that estimate the distribution of fitness effects from biological sequence ...the distribution of S from DNA sequence data has been based at the population level ...allele ... See full document
15
Computationally tractable fitting of graphical models : the cost and benefits of decomposable Bayesian and penalized likelihood approaches : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Albany, New Zealand
... in data with a multivariate normal ...high-dimensional data than sample correlations. This raises the question of which method one should use to model and estimate the ...model ... See full document
157
Penalized likelihood estimation of a trivariate additive probit model
... a penalized likelihood method to estimate a trivariate probit model, which accounts for several types of covariate effects (such as linear, nonlinear, random and spatial effects), as well as ... See full document
29
Challenges in estimating insecticide selection pressures from mosquito field data.
... of selection acting on resistance alleles, their contributions to fitness in heterozygotes (dominance) and their initial frequencies, as a means to predict the rate of spread of resistance in natural ...aegypti. ... See full document
11
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... degrees of nonlinearity across the three methods. The pointwise confidence in- tervals of bivariate probit contain the zero line, suggesting that neither age nor educ have (non-linear or linear) effects. These results ... See full document
25
A penalized likelihood estimation approach to semiparametric sample selection binary response modeling
... degrees of nonlinearity across the three methods. The pointwise confidence in- tervals of bivariate probit contain the zero line, suggesting that neither age nor educ have (non-linear or linear) effects. These results ... See full document
25
On the Statistical Interpretation of Site-Specific Variables in Phylogeny-Based Substitution Models
... on penalized likelihood or smoothing might be applicable and pertinent in extensive parameterization applications, but perhaps the simplest alternative is to adopt a random variable interpretation instead ... See full document
8
Penalized Likelihood Analysis of Haplotype Effect.
... applying penalized likelihood methods to identify important haplotypic factors has become increasingly popular in the ...and selection operator (LASSO) [Tibshirani, 1996] to perform selection ... See full document
127
Penalized likelihood estimation of a trivariate additive probit model
... a penalized likelihood method to estimate a trivariate probit model, which accounts for several types of covariate effects (such as linear, nonlinear, random and spatial effects), as well as ... See full document
29
Joint Variable Selection of Mean Covariance Model for Longitudinal Data
... efficient penalized likelihood based method to select important explanatory variables that make a significant contribution to the joint modelling of mean and covari- ance structures for longitudinal ... See full document
9
Confidence sets based on penalized maximum likelihood estimators
... di¤er from the analysis in the present paper only in that the estimator ^ 2 de…ned below would be replaced by the usual residual variance estimator from the least-squares regression; this would have no ... See full document
29
Minimax Adaptive Estimation of Nonparametric Hidden Markov Models
... marginal distribution of three consecutive observations on Y 3 (where Y is the observation space), for which, roughly speaking, when Y is a subset of R, the optimal rate is N −s/ (2 s +3) , N being the number of ... See full document
43
Evaluation of three methods for estimating the Weibull distribution parameters of Chinese pine (Pinus tabulaeformis )
... Weibull distribution in forestry ...this method has an advantage in com- putation that most of the statistical software packages currently available (S-Plus, SAS, SPSS, …) support the least-squares ... See full document
6
Bayesian adaptive lassos with non convex penalization
... subset selection procedures are usually computationally too time consuming and perhaps more importantly suffer from inherent instability (Breiman, ...variable selection (SSVS) methods have be- come ... See full document
31
Risk and protective factors for psychopathology in adolescence
... Block 1: Method = Forward Stepwise Likelihood Ratio Iteration History" ." Coefficients -2 Log likelihood... Method: Forward Stepwise Likelihood Ratio b.[r] ... See full document
401
Penalized likelihood estimation of a trivariate additive probit model
... GHK method uses the most recent positive-definite estimate of the correlation matrix; this runs the risk of delivering es- timates that are far from the optimal ...correlation coefficients, we ... See full document
69
From Selection to Coronation of Monarchs in the United Kingdom and the Kingdom of Nikki: A Critical Exploration
... known from now to many generations to come and nothing can ...Kingdom, from eighteen (18) years old you can be King or Queen; contrary to the Kingdom of Nikki where the candidate should have seventy (70) ... See full document
6
Improving the accuracy of likelihood based inference in meta analysis and meta regression
... Meta-analysis is a widely applicable approach to combining information from different studies about a common effect of interest. A popular framework for accounting for the heterogeneity between studies is the ... See full document
25
A Penalized Likelihood Method for Mapping Epistatic Quantitative Trait Loci With One-Dimensional Genome Searches
... 3 is a good choice. ground. In contrast, if almost all markers are used as Example III: This example concerns three QTL with cofactors in the model to control the genetic back- only epistatic interactions and no main ... See full document
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