Results 261 to 270 of about 17,239,975 (301)

Problems in regression modeling of randomized clinical trials

Int. Journal of Clinical Pharmacology and Therapeutics, 2005
Data modeling can be applied to improving the precision of clinical studies and multiple regression modeling is increasingly used for this purpose.To assess the uncertainties and risks of misinterpretations commonly encountered in regression analyses and rarely communicated in research papers.Regression analyses add uncertainties to the data in the ...
openaire   +2 more sources

Weighted Random Regression Models and Dropouts

Drug Information Journal, 2004
In studies with repeated measurements, one of the popular primary interests is the comparison of the rates of change in a response variable between groups. The random regression model (RRM) has been offered as a potential solution to statistical problems posed by dropouts in clinical trials.
Chul Ahn, Sin-Ho Jung, Seung-Ho Kang
openaire   +1 more source

Copula-based regression models with data missing at random

Journal of Multivariate Analysis, 2020
Given \(Y\) a regressand and \(\mathbf{W}=(W_1,\dots,W_d)^\top\) \(d\)-dimensional regressors, the generalized regression model \(\min_{a \in\mathbb{R}}\mathbf{E}[L\{g(Y)-a\}|\mathbf{W}=\mathbf{w}]\) is considered, where \(\mathbf{E}\) denotes mathematical expectation, \(g(Y)\) is a known function of \(Y\), and \(L(v)\) is a loss function whose ...
Shigeyuki Hamori, Kaiji Motegi, Zheng Zhang   +2 more
openaire   +3 more sources

Formulation of Fuzzy Random Regression Model

2011
In real-world regression analysis, statistical data may be linguistically imprecise or vague. Given the co-existence of stochastic and fuzzy uncertainty, real data cannot be characterized by using only the formalism of random variables.
Junzo Watada, Shuming Wang, Witold Pedrycz   +2 more
openaire   +1 more source

Regression models for positive random variables

Journal of Econometrics, 1990
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
McDonald, James B., Butler, Richard J.
openaire   +2 more sources

Regression analysis: Random effects models

2016
AbstractThis chapter considers model formulation and interpretation, estimation and testing in regression equations with random intercept heterogeneity. Compared with Chapter 2, assumptions are strengthened and the parametrization made more parsimonious.
Erik Biørn, Erik Biørn, Erik Biørn
openaire   +1 more source

Bayesian model selection for logistic regression models with random intercept

Computational Statistics & Data Analysis, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Helga Wagner, Christine Duller
openaire   +3 more sources

Semiparametric random coefficient regression models

Annals of the Institute of Statistical Mathematics, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

Random effects Weibull regression model for occupational lifetime

European Journal of Operational Research, 2009
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andreas Wienke, Oliver Kuss
openaire   +2 more sources

Goodness of Fit of Logistic Regression Models for Random Graphs

Journal of Computational and Graphical Statistics, 2018
The logistic regression model constitutes a natural and simple tool to understand how covariates (when available) contribute to explain the topology of a binary network. After estimating the logistic parameters, one of the main questions which arises in practice is to assess the goodness of fit of the corresponding model.
Latouche, Pierre, Robin, Stephane, Ouadah, Sarah   +2 more
openaire   +2 more sources