between error estimation and data-based complexity penalization: any good error estimate may be converted into a data …

This paper is concerned with the Bayesian estimation and comparison of flexible, high dimensional multivariate time … special cases, is developed that encompasses estimation, filtering and model choice. The centerpieces of the estimation …

Shrinkage estimation of the covariance matrix of asset returns was introduced to the finance profession several years … measure for covariance matrix estimation with insufficient observations of the underlying variables. The approach is about … paper presents, from a pedagogic perspective, an introduction to shrinkage estimation and uses Microsoft ExcelTM for its …

statistical significance and practical significance. We perform estimation via an empirical Bayes data analysis methodology that …

standard and international trade. The estimation results allow us to formulate some interesting policy conclusions. …

Terms— Model order estimation, model selection, information theoretic criteria, bootstrap 1. INTRODUCTION Several model …], normalized maximum likelihood (NML) [4], Hannan-Quinn criterion (HQC) [5], conditional model-order estimation (CME) [6], and the … model order estimation is at the expense of additional computational complexity. …

Biased estimation has the advantage of reducing the mean squared error (MSE) of an estimator. The question of interest … is how biased estimation affects model selection. In this paper, we introduce biased estimation to a range of model … and unbiased estimation and compare it against modern model selection criteria such as Kay's conditional model order …