search for: frequentist

I apologize if this question is not completely appropriate for this list. I have been using SAS for a while and am now in the process of learning some C and R as a part of my graduate studies. All of the statistical packages I have used generally yield p-values as a default output to standard procedures. This week I have been reading "Testing Precise Hypotheses" by J.O. Berger

Members of this mailing list may be interested in the following new book: Beginner's Guide to GLM and GLMM with R. - A frequentist and Bayesian perspective for ecologists - Zuur AF, Hilbe JM and Ieno EN This book is only available from: http://www.highstat.com/BGGLM.htm This book presents Generalized Linear Models (GLM) and Generalized Linear Mixed Models (GLMM) based on both frequency-based and Bayesian concepts. Usin...

...ere is that the more you go into details, the less canonicity to be found. if hardly anyone has more than one heart (and who has none, except for patients under surgery?), hardly any two people will have the same pattern of capillary vessels in a particular location in their body. that is, on the frequentist reading of 'canonicity', the gross level canonical descriptions correspond to strong accuracy of expectations, the more detailed levels correspond to weaker accuracy of expectations. i have read the article on canonicity writen by fabian et al., and besides its logical clarity, i found the...

...website: http://www.highstat.com/BGZIM.htm Paperback or EBook can be order (exclusively) from: http://www.highstat.com/bookorder.htm TOC: http://www.highstat.com/BGS/ZIM/pdfs/TOCOnly.pdf Keywords: 430 pages. Zero inflated count data. Zero inflated continuous data. Zero inflated proportional data. Frequentist and Bayesian approaches. Random effects. Introduction to Bayesian statistics and MCMC. JAGS. Bayesian model selection. Multivariate GLMM. R code and data sets available. ----------------------------------------------------- Outline The minimum prerequisite for Beginner's Guide to Zero-Infla...

...nd not the group SDs, or even the group Ns. These cannot be reconstructed from the test statistic and df, because the df are already pooled, except under a very strict assumption of equality of groups and variances. I need these summary statistics for a package that performs Bayesian inference for frequentist analyses, through normal approximation of the posterior. To make the package as user friendly as possible, I would like it to have S3 methods for commonly used frequentist analyses in R, such as: lm and t.test. As per the R-project feature request guidelines, I would like to gauge how people feel...

...R2 is good enough for the model selection knowing the candidate models both have two parameters (so no to care about the principle of parsimony) and my guess is that the models needs to have the same form (which is not the case here: linear form vs exponential form) ) or nested to be compared with frequentist or Bayesian approaches such as the AIC and BIC criterion . Thank you very much in advance Armel [[alternative HTML version deleted]]

Hi! I am trying to calculate HPD for the coeficients of regression models fitted with lm or lmrob in R, pretty much in the same way that can be accomplished by the association of mcmcsamp and HPDinterval functions for multilevel models fitted with lmer. Can anyone point me in the right direction on which packages/how to implement this? Thanks for your time! R. [[alternative HTML version

Hi, I'm attending summer School at UCLA (IPAM) on "probabilistics models of cognition". I have been an R-user since v. 1.4.1, but was trained in the frequentist tradition (as most psychologists!). I found that all faculty here use matlab and Murphy's bayes net toolbox. I have not had the need to use matlab before, and would love to stick to R for graphics models and bayesian modeling in general (even if it takes me extra time to cross-code the...

This question is slightly off topic, but I'll use R to try and make it as relevant as possible. I'm working on a problem where I want to compare estimates from a posterior distribution with a uniform prior with those obtained from a frequentist approach. Under these conditions the estimates should agree. Specifically, I am asking the question, "What is the probability that the true proportion of students passing a test is 50% when the observed proportion for that school is only 38%?" For my example, there are 100 students in t...

...der et al., 2009) statistical manuals for psychologists barely mention them. This manual provides a full bayesian toolbox for commonly encountered problems in psychology and social sciences, for comparing proportions, variances and means, and discusses the advantages. But all foundations of the frequentist approach are also provided, from data description to probability and density, through combinatorics and set algebra. A special emphasis has been put on the analysis of categorical data and contingency tables. Binomial and multinomial models with beta and Dirichlet priors are presented, and the...

...alifications: Ph.D. or Master?s in Statistics At least 8 years of experience Prior experience in R, S, SAS and/or S-plus and Design of Experiments (DOE) Prior Data Mining knowledge (PLS, neural networks, OLAP, etc.) Breadth of statistical approaches (Q Value/false discovery rate, P Value, Bayesian, Frequentist, Monte Carlo Methods, multivariate data analysis, logistic regression, chi-squared, Random Forest (RF) predictors) Prior experience in a Life Sciences research and development environment Bioinformatics knowledge preferable Ability to lead or direct the work of others Characteristics: Naturally cr...

Fractional polynomials ("FPs") are an automatic way of fitting non-linear, parametric effects. The R-package mfp implements a frequentist inference approach for FP models. Recently, we have proposed a Bayesian inference approach for normal FP models, which is based on the quasi-default hyper-/g/ prior for the regression coefficients [1]. This approach is implemented in the new R-package "bfp". The R-package bfp (current...

..." becomes quite fraught if there is severe imbalance; e.g., some of the items that contribute to the estimate based on much more data than others. Subject to such caveats as just noted, I'd expect that credible intervals derived from the posterior distributions would be close to the usual frequentist confidence intervals. The main effect of the non-normality may be that the estimates are "inefficient", i.e., the variance may be larger, or the distributions more dispersed, than for "true" maximum likelihood estimates, were you able to obtain them! John Maindonald John Maind...

...inuous) time survival data with penalized regression methods. If you are looking for a bona fide Bayesian survival analysis method and do not wish to spend a lot of time coming up and debugging your MCMC implementations in WinBUGS/JAGS/OpenBUGS this would be the way to go. If you are strictly after frequentist analyses then you can still run them with BayesX (look at the REML chapter in the manual). Christos Argyropoulos > Date: Mon, 3 May 2010 23:18:28 +0200 > From: dutangc@gmail.com > To: r-help@r-project.org > Subject: [R] extended Kalman filter for survival data > > Dear all,...

Fractional polynomials ("FPs") are an automatic way of fitting non-linear, parametric effects. The R-package mfp implements a frequentist inference approach for FP models. Recently, we have proposed a Bayesian inference approach for normal FP models, which is based on the quasi-default hyper-/g/ prior for the regression coefficients [1]. This approach is implemented in the new R-package "bfp". The R-package bfp (current...

...ysis and Monte Carlo simulation modeling. The focus of the course is on how to conduct accurate and effective quantitative risk analyses, including best practices of risk modeling, selecting the appropriate distribution, using data and expert opinion, and avoiding common mistakes. Both Bayesian and frequentist methods will be discussed. Prior experience using R or other simulation tools is not required. For additional information and to register please visit: http://www.epixanalytics.com/quantitative-risk-analysis-with-r-4-days.html To register by phone or for any questions please contact: Barbara O'...

...ve Risk Analysis with R May 1-4, 2018 Fort Collins, Colorado, USA Join us this spring for our QRA with R training. Our class will focus on applied risk modeling methods using the R statistical language and will cover the core principles of QRA and Monte Carlo simulation modeling. Both Bayesian and frequentist methods will be discussed. This class is very popular with a variety of participants, including those without prior risk analysis experience; current R users who would like to build risk analysis and/or simulation models without using commercial tools; risk analysts who would like to migrate sprea...

...) for lm/lmer-objects R To: r-help@stat.math.ethz.ch Message-ID: <4953D638.4090507@anicca-vijja.de> Content-Type: text/plain; charset=ISO-8859-15 Dear R-List, I am interested in the Bayesian view on parameter estimation for multilevel models and ordinary regression models. AFAIU traditional frequentist p-values they give information about p(data_or_extreme|H0). AFAIU it further, p-values in the Fisherian sense are also no alpha/type I errors and therefor give no information about future replications. However, p(data_or_extreme|H0) is not really interesting for social science research questions...

What objective function is maximized by survreg with the default Weibull model? I'm getting finite parameters in a case that has the likelihood maximzed at Infinite, so it can't be a simple maximum likelihood. Consider the following: ############################# > set.seed(3) > Stress <- rep(1:3, each=3) > ch.life <- exp(9-3*Stress) > simLife <- rexp(9,

...impact. If Y happened to have an impact of $500m, then a reasonable approach might be to reconsider and find an additional expert, say Doug B, to confirm. Alternatively, if you don't believe in expert opinions (or subjective probability, or mechanistic modeling), and feel like an empirical frequentist, you might just get a team of monkeys to verify that X in R seems correct, based on a project management strategy that incorporates someone's favorite IT risk mitigation approach. With respect to Bert's points about 21CFR Part 11, please read the documents on the R WWW with respect to s...