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Exploring the impact of post-training rounding in regression models
Post-training rounding, also known as quantization, of estimated parameters stands as a widely adopted technique for mitigating energy consumption...
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Ridge estimation of covariance matrix from data in two classes
This paper deals with the problem of estimating a covariance matrix from the data in two classes: (1) good data with the covariance matrix of...
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An Extension of the Non-central Wishart Distribution with Integer Shape Vector
This research paper deals with an extension of the non-central Wishart introduced in 1944 by Anderson and Girshick, that is the non-central Riesz...
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Enhanced optimal delaunay triangulation methods with connectivity regularization
In this paper, we study the underlying properties of optimal Delaunay triangulations (ODT) and propose enhanced ODT methods combined with...
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A GMM approach in coupling internal data and external summary information with heterogeneous data populations
Because of advances in data collection and storage, statistical analysis in modern scientific research and practice now has opportunities to utilize...
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Heteroscedastic Laplace mixture of experts regression models and applications
Mixture of Experts (MoE) regression models are widely studied in statistics and machine learning for modeling heterogeneity in data for regression,...
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Oracle Inequality for Sparse Trace Regression Models with Exponential β-mixing Errors
In applications involving, e.g., panel data, images, genomics microarrays, etc., trace regression models are useful tools. To address the...
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Some remarks on comparison of predictors in seemingly unrelated linear mixed models
In this paper, we consider a comparison problem of predictors in the context of linear mixed models. In particular, we assume a set of m different...
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Statistical Inferences in a Partially Linear Model with Autoregressive Errors
In this paper, we consider the statistical inferences in a partially linear model when the model error follows an autoregressive process. A two-step...
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On eigenvalues of a high-dimensional Kendall’s rank correlation matrix with dependence
In this paper, we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation matrix. The underlying population...
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Selection of Fixed Effects in High-dimensional Generalized Linear Mixed Models
The selection of fixed effects is studied in high-dimensional generalized linear mixed models (HDGLMMs) without parametric distributional assumptions...
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Variable selection for skew-normal mixture of joint location and scale models
Although there are many papers on variable selection methods based on mean model in the finite mixture of regression models, little work has been...
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Variance Variation Criterion and Consistency in Estimating the Number of Significant Signals of High-dimensional PCA
In this paper, we propose a criterion based on the variance variation of the sample eigenvalues to correctly estimate the number of significant...
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Optimal decorrelated score subsampling for generalized linear models with massive data
In this paper, we consider the unified optimal subsampling estimation and inference on the low-dimensional parameter of main interest in the presence...
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Changepoint Estimation for Dependent and Non-Stationary Panels
The changepoint estimation problem of a common change in panel means for a very general panel data structure is considered. The observations within...
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Bayesian Estimation of the Precision Matrix with Monotone Missing Data
Abstract. This research paper stands for the estimation of the precision matrix of the normal matrix with monotone missing data. We explicitly...
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Stable correlation and robust feature screening
In this paper, we propose a new correlation, called stable correlation, to measure the dependence between two random vectors. The new correlation is...
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Robust reduced rank regression in a distributed setting
This paper studies the reduced rank regression problem, which assumes a low-rank structure of the coefficient matrix, together with heavy-tailed...
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The EM algorithm for ML Estimators under nonlinear inequalities restrictions on the parameters
One of the most powerful algorithms for obtaining maximum likelihood estimates for many incomplete-data problems is the EM algorithm. However, when...
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Weighted Lasso estimates for sparse logistic regression: non-asymptotic properties with measurement errors
For high-dimensional models with a focus on classification performance, the ℓ 1 -penalized logistic regression is becoming important and popular....