Results 251 to 260 of about 460,060 (291)
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Predictive inference on equicorrelated linear regression models
Applied Mathematics and Computation, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shahjahan Khan, M. Ishaq Bhatti
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Order-Restricted Inferences in Linear Regression
Journal of the American Statistical Association, 1995Abstract Regression analysis constitutes a large portion of the statistical repertoire in applications. In cases where such analysis is used for exploratory purposes with no previous knowledge of the structure, one would not wish to impose any constraints on the problem.
Hari Mukerjee, Renjin Tu
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Likelihood Inference for Linear Regression Models
Biometrika, 1988A linear regression model with p regression coefficients \(\beta_ j\) \((j=1 ...
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Inference for a linear regression model with an interval‐censored covariate
Statistics in Medicine, 2003AbstractInterval‐censored observations of a response variable are a common occurrence in medical studies, and usually result when the response is the elapsed time until some event whose occurrence is periodically monitored. In this paper we consider a multivariate regression setting in which the explanatory variable is interval censored.
Guadalupe, Gómez +2 more
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Inference in generalized linear regression models with a censored covariate
Computational Statistics & Data Analysis, 2012zbMATH Open Web Interface contents unavailable due to conflicting licenses.
John V. Tsimikas +2 more
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Robust Inference in Conditionally Linear Nonlinear Regression Models
Scandinavian Journal of Statistics, 2008Abstract. We consider robust methods of likelihood and frequentist inference for the nonlinear parameter, sayα, in conditionally linear nonlinear regression models. We derive closed‐form expressions for robust conditional, marginal, profile and modified profile likelihood functions forαunder elliptically contoured data distributions.
Paige, Robert L., Fernando, P. Harshini
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Variational Inference of Linear Regression with Nonzero Prior Means
Communications in Statistics - Simulation and Computation, 2014In this article, we employ the variational Bayesian method to study the parameter estimation problems of linear regression model, wherein some regressors are of Gaussian distribution with nonzero prior means. We obtain an analytical expression of the posterior parameter distribution, and then propose an iterative algorithm for the model.
Zijian Dong, Zhongming Wang
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Inference After Variable Selection in Linear Regression Models
Biometrika, 1992Summary: We explore the impact of variable selection on statistical inferences in linear regression models. In particular, the generalized final prediction error criterion of \textit{R. Shibata} [ibid. 71, 43-49 (1984; Zbl 0543.62053)] is considered and it is found, among other things, that inferences on the regression coefficients are impaired by the ...
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The Impact of Model Selection on Inference in Linear Regression
The American Statistician, 1990Abstract Model selection and inference are usually treated as separate stages of regression analysis, even though both tasks are performed on the same set of data. Once a model has been selected, one typically proceeds as though one has a fresh data set generated by the selected model.
Clifford M. Hurvich, Chih—Ling Tsai
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Jackknife inference for heteroscedastic linear regression models
Canadian Journal of Statistics, 1993AbstractInference on the regression parameters in a heteroscedastic linear regression model with replication is considered, using either the ordinary least‐squares (OLS) or the weighted least‐squares (WLS) estimator. A delete‐group jackknife method is shown to produce consistent variance estimators irrespective of within‐group correlations, unlike the ...
Shao, Jun, Rao, J. N. K.
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