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On priors which give Bayes minimax estimators of Baranchik’s form
We study the construction of prior distributions which give Bayes minimax estimators of a normal mean vector. Particular attention is paid to priors...
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Minimax weight learning for absorbing MDPs
Reinforcement learning policy evaluation problems are often modeled as finite or discounted/averaged infinite-horizon Markov Decision Processes...
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Minimax estimation for time series models
The minimax principle is very important for all the fields of statistical science. The minimax approach is to choose an estimator which protects...
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Minimax robust designs for regression models with heteroscedastic errors
Minimax robust designs for regression models with heteroscedastic errors are studied and constructed. These designs are robust against possible...
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The Minimax Principle
The criteria discussed so far, unbiasedness and invariance, suffer from the disadvantage of being applicable, or leading to optimum solutions, only...
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A Minimax Testing Perspective on Spatial Statistical Resolution in Microscopy
Ever since Ernst Abbe first stated his resolution criterion for light microscopy in his seminal 1873 paper “Beiträge zur Theorie des Mikroskops und...
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Obtaining minimax lower bounds: a review
Minimax lower bounds determine the complexity of given statistical problems by providing fundamental limit of any procedures. This paper gives a...
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Optimal Design Theory for Linear Models
This chapter establishes the theory for linear models, concepts, and results, and provides the most important techniques to do research in this area...
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Simple Adaptive Estimation of Quadratic Functionals in Nonparametric IV Models
This paper considers adaptive, minimax estimation of a quadratic functional in a nonparametric instrumental variables (NPIV) model, which is an...
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On Bayesian predictive density estimation for skew-normal distributions
This paper is concerned with prediction for skew-normal models, and more specifically the Bayes estimation of a predictive density for
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Shrinkage estimation with logarithmic penalties
In this paper, we have developed a novel approach for deriving shrinkage estimators of means without assuming normality. Our method is based on the...
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Robust Optimal Design When Missing Data Happen at Random
In this article, we investigate the robust optimal design problem for the prediction of response when the fitted regression models are only...
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Adaptive Estimation of a Function from its Exponential Radon Transform in Presence of Noise
In this article we propose a locally adaptive strategy for estimating a function from its Exponential Radon Transform (ERT) data, without prior...
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Rate of Convergence
The fundamental requirement in data analysis is the consistent estimation of a parameter. As the sample size increases, the precision of the...
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Simulation comparisons between Bayesian and de-biased estimators in low-rank matrix completion
In this paper, we study the low-rank matrix completion problem, a class of machine learning problems, that aims at the prediction of missing entries...
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Stochastic functional linear models and Malliavin calculus
In this article, we study stochastic functional linear models (SFLM) driven by an underlying square integrable stochastic process X ( t ) which is...
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A Numerical Method for Hedging Bermudan Options under Model Uncertainty
Model uncertainty has recently been receiving more attention than risk. This study proposes an effective computational framework to derive optimal...
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Asymptotic theory in network models with covariates and a growing number of node parameters
We propose a general model that jointly characterizes degree heterogeneity and homophily in weighted, undirected networks. We present a moment...
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Asymptotic theory for regression models with fractional local to unity root errors
This paper develops the asymptotic theory for parametric and nonparametric regression models when the errors have a fractional local to unity root...
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