Results 141 to 150 of about 1,926,955 (188)

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.)
openaire   +2 more sources

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

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

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
openaire   +1 more source

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

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
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

Random weightingT-statistics in linear regression models

Acta Mathematica Sinica, 1995
Summary: We have constructed a random weighting statistic to approximate the distribution of the Studentized least square estimator in a linear regresion model with ideal accuracy \(o(n^{- 1/2})\). Thus, we have provided a more practical distribution approximation method.
Shi, Jian, Zheng, Zhongguo
openaire   +2 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

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