Results 281 to 290 of about 113,075 (317)
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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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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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A Procedure for Robust Estimation and Inference in Linear Regression
1991Even if robust regression estimators have been around for nearly 20 years, they have not found widespread application. One obstacle is the diversity of estimator types and the necessary choices of tuning constants, combined with a lack of guidance for these decisions.
Victor J. Yohai +2 more
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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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Inference for linear regression models in the presence of heteroskedasticity
2017The linear regression model is a highly versatile tool with which one can model a response variable in terms of one or more predictor variables. The classical linear model is based on six primary theoretical assumptions. In this study the main focus is on the assumption of "homoskedasticity" and the violation thereof, called "heteroskedasticity ...
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A study over the general formula of regression sum of squares in multiple linear regression
Numerical Methods for Partial Differential Equations, 2021Mehmet Korkmaz
exaly