Results 151 to 160 of about 1,926,955 (188)
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Bayesian model selection for logistic regression models with random intercept
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Helga Wagner, Christine Duller
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Copula-based regression models with data missing at random
Journal of Multivariate Analysis, 2020Given \(Y\) 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 +2 more
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Semiparametric random coefficient regression models
Annals of the Institute of Statistical Mathematics, 1993zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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A Medical Application of the General Random Coefficient Regression Model
Biometrical Journal, 1990AbstractRCR models are reviewed. Various variance estimators are described, among them a new one. These variance estimators are compared in a simulation study. An obstetric data set is subjected to a detailed analysis by means of RCR techniques. In particular, interval estimation is considered.
Bondeson, J., Lanke, J.
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Estimation of random coefficient regression models
Journal of Statistical Computation and Simulation, 1981Linear 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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On Randomizing Estimators in Linear Regression Models
2000In this work we consider a special kind of randomization in the analysis of linear regression. This randomization is connected to the Δ2-distribution which was first introduced by Ermakov and Zolotukhin (1960) for decreasing the variance in the Monte Carlo calculation of integrals. The resulting resampling procedure allows for separating the systematic
S. Ermakov, R. Schwabe
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Random effects Weibull regression model for occupational lifetime
European Journal of Operational Research, 2009zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Andreas Wienke, Oliver Kuss
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Linear Regression Model with Random Coefficients
Biometrical Journal, 1984AbstractEstimation 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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Goodness of Fit of Logistic Regression Models for Random Graphs
Journal of Computational and Graphical Statistics, 2018The 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 +2 more
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Regression analysis: Random effects models
2016AbstractThis 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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