Results 211 to 220 of about 706,502 (262)

Interpreting Parameters in the Logistic Regression Model with Random Effects

Biometrics, 2000
Summary.Logistic regression with random effects is used to study the relationship between explanatory variables and a binary outcome in cases with nonindependent outcomes. In this paper, we examine in detail the interpretation of both fixed effects and random effects parameters.
Esben Budtz-Joergensen, , K Larsen
exaly   +4 more sources

The Validity of Polynomial Regression in the Random Regression Model

Review of Educational Research, 1978
Sockloff (1976), in reviewing the appropriateness of fixed and random models in regression analysis, has concluded "that the analysis of nonlinearity via polynomial and product regression should be limited to experimental studies" (p. 288), and that, "[r]egarding the analysis of nonlinearity in observational data under the Random Model, the Random ...
Elliot M. Cramer, Mark I. Appelbaum
openaire   +1 more source

Random Regression Coefficient Models

2020
This chapter describes a modification of the nested error regression model having random regression coefficients. We can intuitively expect that the slope parameters of some explanatory variable are not constant and therefore they should take different values in different domains.
Domingo Morales, María Dolores Esteban, Agustín Pérez, Tomáš Hobza   +3 more
openaire   +1 more source

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
openaire   +2 more sources

A regression model for multivariate random length data

Statistics in Medicine, 1999
Multivariate random length data occur when we observe multiple measurements of a quantitative variable and the variable number of these measurements is also an observed outcome for each experimental unit. For example, for a patient with coronary artery disease, we may observe a number of lesions in that patient's coronary arteries, along with ...
H X, Barnhart, A S, Kosinski, A R, Sampson   +2 more
openaire   +2 more sources

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.
openaire   +2 more sources

-optimal designs in random coefficient regression models

Statistics & Probability Letters, 2014
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Liu, Xin, Yue, Rong-Xian, Chatterjee, Kashinath   +2 more
openaire   +1 more source

A Random-Effects Ordinal Regression Model for Multilevel Analysis

Biometrics, 1994
A random-effects ordinal regression model is proposed for analysis of clustered or longitudinal ordinal response data. This model is developed for both the probit and logistic response functions. The threshold concept is used, in which it is assumed that the observed ordered category is determined by the value of a latent unobservable continuous ...
Hedeker, Donald, Gibbons, Robert D.
openaire   +3 more sources

Efficient Inference in a Random Coefficient Regression Model

Econometrica, 1970
Computes a GLS matrix weighted estimator for a panel data set. meangroup.src does a similar estimator, but uses simple weighted average rather than a matrix-weighted average. Swamy(1970), "Efficient Inference in a Random Coefficient Regression Model", Econometrica, vol 38, 311-323. (This abstract was borrowed from another version of this item.)
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Regression Model Based on Fuzzy Random Variables

2008
In real-world regression problems, various statistical data may be linguistically imprecise or vague. Because of such co-existence of random and fuzzy information, we can not characterize the data only by random variables. Therefore, one can consider the use of fuzzy random variables as an integral component of regression problems.
Shinya Imai, Shuming Wang, Junzo Watada
openaire   +1 more source