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Fast and adaptive sparse precision matrix estimation in high dimensions

Liu, Weidong; Luo, Xi - In: Journal of Multivariate Analysis 135 (2015) C, pp. 153-162

This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues....

Persistent link: https://www.econbiz.de/10011189572

Variable selection in high-dimensional quantile varying coefficient models

Tang, Yanlin; Song, Xinyuan; Wang, Huixia Judy; Zhu, Zhongyi - In: Journal of Multivariate Analysis 122 (2013) C, pp. 115-132

models. The proposed method is based on basis function approximation and LASSO-type penalties. We show that the first stage … penalized estimator with LASSO penalty reduces the model from ultra-high dimensional to a model that has size close to the true … model, but contains the true model as a valid sub model. By applying adaptive LASSO penalty to the reduced model, the second …

Persistent link: https://www.econbiz.de/10010702800

Variable selection for general transformation models with right censored data via nonconcave penalties

Li, Jianbo; Gu, Minggao; Zhang, Riquan - In: Journal of Multivariate Analysis 115 (2013) C, pp. 445-456

In this paper, we consider variable selection for general transformation models with right censored data via nonconcave penalties. We will conduct the variable selection by maximizing the penalized log-marginal likelihood function. In the proposed variable selection procedures, we not only can...

Persistent link: https://www.econbiz.de/10010608106

SCAD penalized rank regression with a diverging number of parameters

Yang, Hu; Guo, Chaohui; Lv, Jing - In: Journal of Multivariate Analysis 133 (2015) C, pp. 321-333

In this paper, we study the robust variable selection and estimation based on rank regression and SCAD penalty function in linear regression models when the number of parameters diverges with the sample size increasing. The proposed method is resistant to heavy-tailed errors or outliers in the...

Persistent link: https://www.econbiz.de/10011116251

Lasso penalized model selection criteria for high-dimensional multivariate linear regression analysis

Katayama, Shota; Imori, Shinpei - In: Journal of Multivariate Analysis 132 (2014) C, pp. 138-150

-dimensional multivariate linear regression analysis. The proposed criteria are based on a Lasso type penalized likelihood function to allow the …

Persistent link: https://www.econbiz.de/10010939517

Model selection for integrated autoregressive processes of infinite order

Ing, Ching-Kang; Sin, Chor-yiu; Yu, Shu-Hui - In: Journal of Multivariate Analysis 106 (2012) C, pp. 57-71

We show that Akaike’s Information Criterion (AIC) and its variants are asymptotically efficient in integrated autoregressive processes of infinite order (AR(∞)). This result, together with its stationary counterpart established previously in the literature, ensures that AIC can ultimately...

Persistent link: https://www.econbiz.de/10011042035

A simple and efficient Bayesian procedure for selecting dimensionality in multidimensional scaling

Oh, Man-Suk - In: Journal of Multivariate Analysis 107 (2012) C, pp. 200-209

Multidimensional scaling (MDS) is a technique which retrieves the locations of objects in a Euclidean space (the object configuration) from data consisting of the dissimilarities between pairs of objects. An important issue in MDS is finding an appropriate dimensionality underlying these...

Persistent link: https://www.econbiz.de/10010572282

Model selection in linear mixed effect models

Peng, Heng; Lu, Ying - In: Journal of Multivariate Analysis 109 (2012) C, pp. 109-129

Mixed effect models are fundamental tools for the analysis of longitudinal data, panel data and cross-sectional data. They are widely used by various fields of social sciences, medical and biological sciences. However, the complex nature of these models has made variable selection and parameter...

Persistent link: https://www.econbiz.de/10010572296

Information based model selection criteria for generalized linear mixed models with unknown variance component parameters

Yu, Dalei; Zhang, Xinyu; Yau, Kelvin K.W. - In: Journal of Multivariate Analysis 116 (2013) C, pp. 245-262

This paper derives the corrected conditional Akaike information criteria for generalized linear mixed models by analytic approximation and parametric bootstrap. The sampling variation of both fixed effects and variance component parameter estimators are accommodated in the bias correction term....

Persistent link: https://www.econbiz.de/10010665718