Predictive density estimation under the Wasserstein loss

On Predictive Density Estimation for Location Families under Integrated Absolute Error Loss

Tatsuya Kubokawa, Éric Marchand, William E. Strawderman a Department of Economics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, JAPAN (e-mail: [email protected]) b Université de Sherbrooke, Département de mathématiques, Sherbrooke Qc, CANADA, J1K 2R1 (e-mail: [email protected]) c Rutgers University, Department of Statistics and Biostatistics, 501 Hill Center, Bu...

Stochastic domination in predictive density estimation for ordered normal means under α-divergence loss

On predictive density estimation for location families under integrated squared error loss

Tatsuya Kubokawa, Éric Marchand, William E. Strawderman a Department of Economics, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, JAPAN (e-mail: [email protected]) b Université de Sherbrooke, Département de mathématiques, Sherbrooke Qc, CANADA, J1K 2R1 (e-mail: [email protected]) c Rutgers University, Department of Statistics and Biostatistics, 501 Hill Center, Bu...

Admissible Predictive Density Estimation

Let X|μ ∼ Np(μ,vxI ) and Y |μ ∼ Np(μ,vyI ) be independent pdimensional multivariate normal vectors with common unknown mean μ. Based on observing X = x, we consider the problem of estimating the true predictive density p(y|μ) of Y under expected Kullback–Leibler loss. Our focus here is the characterization of admissible procedures for this problem. We show that the class of all generalized Baye...

Density Estimation Under Constraints

We suggest a general method for tackling problems of density estimation under constraints. It is, in effect, a particular form of the weighted bootstrap, in which resampling weights are chosen so as to minimize distance from the empirical or uniform bootstrap distribution subject to the constraints being satisfied. A number of constraints are treated as examples. They include conditions on mome...