Results 271 to 280 of about 1,128,323 (301)

Inference for Generalized partial functional linear regression

Statistica Sinica, 2020
A 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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Statistical Inference for High-Dimensional Generalized Linear Models With Binary Outcomes

Journal of the American Statistical Association, 2021
This article develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered.
T. Cai, Zijian Guo, Rong Ma
semanticscholar   +1 more source

Constrained inference in linear regression

Journal of Multivariate Analysis, 2016
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Thelge Buddika Peiris, Bhaskar Bhattacharya   +1 more
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Prediction of the unconfined compressive strength of stabilised soil by Adaptive Neuro Fuzzy Inference System (ANFIS) and Non-Linear Regression (NLR)

Geomechanics and Geoengineering, 2019
This paper describes the effect of stabilizer content, curing time and moisture content on the UCS based upon 150 samples of stabilized soil. The results indicate an optimum value of lime or cement content which corresponds to the maximum UCS.
Mohsen Saadat, M. Bayat
semanticscholar   +1 more source

Heteroskedasticity-Robust Inference in Linear Regressions

Communications in Statistics - Simulation and Computation, 2009
The 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, Tatiene C. Souza, Francisco Cribari-Neto, Gilênio B. Fernandes   +3 more
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Order-Restricted Inferences in Linear Regression

Journal of the American Statistical Association, 1995
Abstract 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, 2008
Abstract. 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, 1988
A linear regression model with p regression coefficients \(\beta_ j\) \((j=1 ...
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