Results 241 to 250 of about 1,906,785 (292)

Random effects in ordinal regression models

Computational Statistics & Data Analysis, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Tutz, Gerhard, Hennevogl, Wolfgang
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Prediction in Random Coefficient Regression Models

Biometrical Journal, 1990
AbstractMuch attention has been given to the problem of predicting future observations for some individual within a random coefficient regression (RCR) model, using the previous observations on that individual as well as the information from the rest of the data material.
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Regression models for Boolean random sets

Journal of Applied Statistics, 2006
Abstract In this paper we consider the regression problem for random sets of the Boolean-model type. Regression modeling of the Boolean random sets using some explanatory variables are classified according to the type of these variables as propagation, growth or propagation-growth models.
M. Khazaee, K. Shafie
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Semiparametric random coefficient regression models

Annals of the Institute of Statistical Mathematics, 1993
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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
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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
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LEARNING RANDOM MODEL TREES FOR REGRESSION

International Journal of Computers and Applications, 2011
AbstractRegression is one of the most important tasks in real-world data mining applications. Among a large number of regression models, model tree is an excellent regression model. In this paper, we single out an improved model tree algorithm via introducing randomness into the process of building model trees.
Chaoqun Li, Hongwei Li
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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.
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Drop-outs and a random regression model

Journal of Biopharmaceutical Statistics, 1997
The implications of drop-outs for power of random regression model (RRM) tests of significance for differences in the rate of change produced by two treatments in a randomized parallel-groups design were investigated by Monte Carlo simulation methods. The two-stage RRM fitted a least squares linear regression equation to all of the available data for ...
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A random‐effects regression model for meta‐analysis

Statistics in Medicine, 1995
AbstractMany meta‐analyses use a random‐effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random‐effects regression approach for the synthesis of 2 × 2 tables allows the inclusion of covariates that may explain heterogeneity.
C S, Berkey, D C, Hoaglin, F, Mosteller, G A, Colditz   +3 more
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