[PDF] Top 20 Tests for High Dimensional Generalized Linear Models
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Tests for High Dimensional Generalized Linear Models
... and linear regression models. The proposed tests are designed to improve the performance of Goeman et ...the high dimensionality can insert adverse influence on the test of Goeman et ...the ... See full document
39
Mean and median bias reduction in generalized linear models
... This paper presents a unified approach for mean and median bias reduction (BR) in GLMs using adjusted score functions (Firth 1993; Kosmidis and Firth 2009; and Kenne Pagui et al. 2017, respectively). Specifically, Firth ... See full document
17
MCP penalized Regression in High Dimensional Partially Linear Models for Right Censored Data
... further generalized these results to a semi-parametric regression model whose covariate effects have two ...low dimensional covariates and takes a nonparametric form, and the second part is for the ... See full document
8
Generalized Inference in Linear Regression Models
... of generalized confidence inter- vals (GCIs) of linear regression coefficients and dispersion parameters and generalized tests (GTs) for comparing regression coefficients for small and ... See full document
106
Adaptive group bridge estimation for high dimensional partially linear models
... This paper studies group selection for the partially linear model with a diverging number of parameters. We propose an adaptive group bridge method and study the consistency, convergence rate and asymptotic ... See full document
18
Fitting Models of Vulnerability to Toxicity with Generalized Linear Models
... results from his/her prolonged exposure to toxic substances. The individual will be ‘pronounced’ toxic, with respect to the toxic substance, if the estimated quantity of the substance found in the samples (e.g. blood or ... See full document
12
Automatic Variable Selection for High Dimensional Linear Models with Longitudinal Data
... smooth-threshold generalized estimating equation based on quadratic infe- rence function (SGEE-QIF) to the high-dimensional longitudinal ...the high/ultra-high dimension varying ... See full document
11
Sparse Linear Models and l1−Regularized 2SLS with High Dimensional Endogenous Regressors and Instruments
... for linear triangular models where the number of endogenous regressors in the main equation and the number of instruments in the first- stage equations can exceed the sample size n, and the regression ... See full document
38
Sparse Linear Models and l1−Regularized 2SLS with High Dimensional Endogenous Regressors and Instruments
... In addition to the research directions already proposed in the previous sections for the future, we discuss some more extensions in the following. First, as pointed out by a reviewer, it would be ideal to test the ... See full document
42
Ridge regression and diagnostics in generalized linear models
... of generalized linear models (GLMs) introduced by Neider & Wedderburn (1972), the explanatory variables are highly correlated and so are termed collinear or ...the linear regression case ... See full document
133
Quasi Monte Carlo Estimation in Generalized Linear Mixed Model with Correlated Random Effects
... Generalized linear mixed models (GLMMs) are very useful for non-Gaussian correlated or clustered data and widely applied in many areas including epidemiology, ecological, and clinical ...hierarchical ... See full document
16
Linear Kernel Tests via Empirical Likelihood for High-Dimensional Data
... and the proposed ELR-MMD work well for d = 10, 50, 100, but FSSD shows poor rejection rates for d = 10, and fails to reject the null hypothesis for d = 50, 100, even when the variance v is very large. We also increase an ... See full document
8
Statistical Inference For High-Dimensional Linear Models
... for high-dimensional linear models have been actively studied, where the focus is on the construction of confidence intervals for individual coordinates (Javan- mard & Montanari, 2014a; ... See full document
253
No penalty no tears : least squares in high dimensional linear models
... fitting high-dimensional sparse linear mod- els by reconsidering OLS and answering the following simple question: Can ordinary least squares consistently fit these models with some suitable ... See full document
27
Score tests in generalized linear measurement error models
... The solid line is the density estimate based 01\ all the data, while the dashed line is the estimate for the 37 cases of breast. cancer[r] ... See full document
21
A study of partial F tests for multiple linear regression models
... It is noteworthy that one may obtain the p-values corresponding to the partial F test and the test induced by the restricted band. In fact, when k ≥ 3, plots of confidence bands may not be easily available even though we ... See full document
26
Two Sample Tests for High Dimensional Covariance Matrices
... of high-dimensional covariances in Huang, Liu, Pourahmadi and Liu (2006) and Rothman, Levina and Zhu ...on high-dimensional covariance ... See full document
41
High dimensional painlev´e integrable schwarzian boussinesq models
... In summary, we have extended the (1+1)-dimensional Schwartzian Boussinesq equation to the arbitrary dimensional system with selecting the high dimensional Schwarzian derivatives. We have shown ... See full document
5
Fit Generalized Linear Models by Using of Different Likelihoods
... where g(.) is a known function called the link function (because it links the mean of the y i s and the linear predictors), X i and are the ith row of the model matrix and parameter vector for the ... See full document
5
M estimation in high dimensional linear model
... ultra high-dimensional ...local linear approximation method (LLA) to prove that the obtained estimator enjoyed good asymptotic properties, and they demonstrated that this method improved the ... See full document
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