Results 251 to 260 of about 1,906,785 (292)

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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Optimal Designs in Random Coefficient Linear Regression Models

Calcutta Statistical Association Bulletin, 1996
In this paper we consider a random coefficients regression model in the context of repeated measurements. The measurements are taken at consecutive points for several experimental units, and the total number of measurements have a fixed upper bound.
Liski, Erkki P., Luoma, Arto, Sinha, Bikas K.   +2 more
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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
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Analysis of longitudinal data: Random coefficient regression modelling

Statistics in Medicine, 1994
AbstractWe review random coefficient regression (RCR) models and methods for fitting these models from an applications perspective. Methods for data with exponential family distributions are presented with the Gaussian distribution as a special case.
C M, Rutter, R M, Elashoff
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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
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Estimation of random coefficient regression models

Journal of Statistical Computation and Simulation, 1981
Linear regression models with coefficients across individual units regarded as random samples from some population are studied in this article from a Bayesian viewpoint. A prior distribution of the secondary parameters is derived following the Jeffreys rule. Posterior distribution of the primary and secondary parameters, and the predictive distribution
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Multivariate Parametric Random Effect Regression Models for Fecundability Studies

Biometrics, 2000
Summary. Delay until conception is generally described by a mixture of geometric distributions. Weinberg and Gladen (1986, Biometrics42, 547–560) proposed a regression generalization of the beta‐geometric mixture model where covariates effects were expressed in terms of contrasts of marginal hazards.
Ecochard, René, Clayton, David G.
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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.
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Nonparametric Regression-Spline Random Effects Models [PDF]

, 2015
We consider a B-spline regression approach towards ecient nonparametric modelling of a random effects (error component) model. Theoretical underpinnings are provided, finite-sample performance is evaluated via Monte Carlo simulation, and an application that examines the contribution of different types of public infrastructure on private production is ...
Shujie Ma, Jeffrey S. Racine, Aman Ullah
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Linear Regression Model with Random Coefficients

Biometrical Journal, 1984
AbstractEstimation of linear regression with random coefficients is studied in this work. Non‐negative estimates of variances are proposed. The result is a modification of works of SRIVASTAVA et al. (1981).
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