Journal ArticleDOI
Simultaneous confidence intervals for multiple comparisons among expected values of log-normal variables
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TLDR
Methods based on normal approximations and generalized pivotal quantities are extended to the case of multiple contrasts and are found to be very liberal and biased with respect to directional type-I-errors.About:
This article is published in Computational Statistics & Data Analysis.The article was published on 2013-02-01. It has received 19 citations till now. The article focuses on the topics: Coverage probability & Multiple comparisons problem.read more
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Journal ArticleDOI
Process analytical technology in continuous manufacturing of a commercial pharmaceutical product.
Jenny M. Vargas,Sarah Nielsen,Vanessa Cárdenas,Anthony Gonzalez,Efrain Y. Aymat,Elvin Almodovar,Gustavo Classe,Yleana Colon,Eric Sanchez,Rodolfo J. Romañach +9 more
TL;DR: The implementation of process analytical technology and continuous manufacturing at an FDA approved commercial manufacturing site is described, and excellent agreement between the mean drug concentration values in the blends and tablets produced provides further evidence of the suitability of the validation strategy that was followed.
Journal ArticleDOI
Simultaneous confidence intervals for ratios of means of several lognormal distributions: A parametric bootstrap approach
TL;DR: The proposed parametric bootstrap method is conceptually simpler than other proposed methods and consistently performs better than other methods: its coverage probability is close to the nominal confidence level and the resulting intervals are typically shorter than the intervals produced by other methods.
Journal ArticleDOI
Simultaneous confidence intervals for comparisons of several multinomial samples
TL;DR: Asymptotic methods are described for constructing simultaneous confidence intervals for this inferential problem and a method based on sampling from the Dirichlet posterior yields simultaneous coverage probabilities closest to the nominal confidence level.
Journal ArticleDOI
Simultaneous confidence intervals for all differences of coefficients of variation of log-normal distributions
TL;DR: In this article, a novel approach was proposed for constructing simultaneous confidence intervals for all differences of coefficients of variation of lognormal distributions, using the method of variance estimates recovery (MOVER) approach and the computational approach.
Book ChapterDOI
Simultaneous Fiducial Generalized Confidence Intervals for All Differences of Coefficients of Variation of Log-Normal Distributions
TL;DR: A Monte Carlo simulation was conducted to evaluate the performance, coverage probability and average length, of the simultaneous confidence intervals of the proposed simultaneous fiducial generalized confidence intervals approach.
References
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R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
Simultaneous inference in general parametric models.
TL;DR: This paper describes simultaneous inference procedures in general parametric models, where the experimental questions are specified through a linear combination of elemental model parameters, and extends the canonical theory of multiple comparison procedures in ANOVA models to linear regression problems, generalizedlinear models, linear mixed effects models, the Cox model, robust linear models, etc.
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A Multiple Comparison Procedure for Comparing Several Treatments with a Control
TL;DR: In this article, a multiple comparison procedure for comparing several treatments with a control is presented, which is based on the Multiple Comparison Procedure for Comparing Several Treatments with a Control (MCPC).
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Bayesian Computation and Stochastic Systems
TL;DR: Basic methodology of MCMC is presented, emphasizing the Bayesian paradigm, conditional probability and the intimate relationship with Markov random fields in spatial statistics, and particular emphasis on the calculation of posterior probabilities.