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Inference for Generalized partial functional linear regression
Statistica Sinica, 2020A penalized likelihood ratio test for generalized partial functional linear models is proposed. A roughness penalty is used to control the model complexity via a smoothing parameter. A new type of inner product is defined. Using this inner product, a Bahadur representation for both functional and scalar penalized estimators is developed based on the ...
Li, Ting, Zhu, Zhongyi
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Constrained inference in linear regression
Journal of Multivariate Analysis, 2016zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thelge Buddika Peiris +1 more
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Heteroskedasticity-Robust Inference in Linear Regressions
Communications in Statistics - Simulation and Computation, 2009The assumption that all errors share the same variance (homoskedasticity) is commonly violated in empirical analyses carried out using the linear regression model. A widely adopted modeling strategy is to perform point estimation by ordinary least squares and then perform testing inference based on these point estimators and heteroskedasticity ...
Verônica M. C. Lima +3 more
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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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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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Likelihood Inference for Linear Regression Models
Biometrika, 1988A linear regression model with p regression coefficients \(\beta_ j\) \((j=1 ...
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