Minimum Risk Estimation of Scalar Means under Convex Combination of Loss Functions

We derive the minimum risk estimates of the scalar means for Normal, Exponential, and Gamma distributions, under the convex combination of SEL and LINEX loss functions. The functional forms of the proposed estimates for the three examples are general in nature, and for the boundary conditions provide us with the corresponding estimates under SEL and LINEX loss, respectively. We authenticate our proposed models using different iterative as well as meta-heuristic techniques, and through extensive simulation as well as application of live data sets, validate the efficacy of our proposed results.     

Estimation for the Multiple Regression Setup Using Balanced Loss Function

Consider the estimation problem for the multiple linear regression (MLR) setup, under the balanced loss function (BLF), where goodness of fit and precision of estimation are modeled using either squared error loss (SEL) or linear exponential (LINEX) loss functions. The authors derive the minimum risk estimates for two different variants of BLF and prove for both the cases the existence of the ubiquitous SEL and LINEX estimates at the boundary conditions. Conclusions draw from the exhaustive simulation runs prove the general nature of proposed theorems.     

Joint Gait-Cadence Analysis for Human Identification Using an Elastic Shape Framework

We perform human identification by gait recognition where subjects' gait is represented by silhouettes which are elements of a manifold of Square-Root Velocity functions. Gait cycles become stochastic processes on this manifold; cadence its rate of execution. Using geometry of this manifold, we compute mean gait cycle templates for subjects. An observation model, where test sequences are random perturbations of templates, produces likelihood functions for classification. We perform temporal registration—linear and nonlinear—of cycles with templates, removing cadence effects. In an experiment on 26 individuals, linear registration, preserving cadence, performs better than nonlinear registration, which removes cadence.     

Health care reform. Pan American Uni-Care Health Insurance Plan

Something is definitely wrong with the American health care system. Too many citizens are denied health care, and health care costs continue to rise at an uncomfortable and intolerable rate. Ensuring care for all is a paramount goal. There is no way to simultaneously cover everyone; leave the reimbursement of physicians unrestrained; ensure instantaneous access to every imaginable high-technology service; subsidize the world's costliest and least efficient health bureaucracy; and contain costs. Widespread dissatisfaction in all quarters--physicians, hospitals, third-party payers, regulators and consumers--has led to an avalanche of reform proposals. Rapidly changing social, political, and economic environments; rising fiscal pressure; and an evolving understanding of the major determinants of health have also created pressure for changes. There are some new and hopeful signs that America is facing up to the need for changes in the health care delivery system. The Pan American Uni-Care Health Plan that is described in this article may serve as a reasonable balance among these competing priorities.     

Pitman nearness comparisons of Stein-type estimators for regression coefficients in replicated experiments

This paper presents a comparative study of the performance properties of one unbiased and two Stein-type estimators for combining the estimates of coefficients in a linear regression model when data sets are available from replicated experiments conducted at possibly different stations.     

Selection buasubg parameters in adaptive ridge regression estimators

Strawderman's family of regression estimators is considered. The choice of the scalars wbich characterize the biasing parameter is studied by obtaining the bias vector and the mean squared error matrix.     

Minimal 2-coverings of a finite affine space based on GF(2)

Let EG(m, 2) denote the m-dimensional finite Euclidean space (or geometry) based on GF(2), the finite field with elements 0 and 1. Let T be a set of points in this space, then T is said to form a q-covering (where q is an integer satisfying 1?q?m) of EG(m, 2) if and only if T has a nonempty intersection with every (m-q)-flat of EG(m, 2). This problem first arose in the statistical context of factorial search designs where it is known to have very important and wide ranging applications. Evidently, it is also useful to study this from the purely combinatorial point of view. In this paper, certain fundamental studies have been made for the case when q=2. Let N denote the size of the set T. Given N, we study the maximal value of m.     

The nature of individual investors' heterogeneous expectations

This article investigates whether heterogeneity in investors' perceptions of the risk/return characteristics of a particular stock and the stock market can be explained in terms of these investors' demographic characteristics (sex, age. income and education). Results indicate that these specific demographic characteristics are not particularly useful in differentiating among investors holding divergent opinions. Hut findings confirm previous results that investors' risk perceptions vary systematically and do not result simply from measurement error.     

EXACT FINITE-SAMPLE PROPERTIES OF A PRE-TEST ESTIMATOR IN RIDGE REGRESSION

This paper discusses a pre-test regression estimator which uses the least squares estimate when it is “large” and a ridge regression estimate for “small” regression coefficients, where the preliminary test is applied separately to each regression coefficient in turn to determine whether it is “large” or “small.” For orthogonal regressors, the exact finite-sample bias and mean squared error of the pre-test estimator are derived. The latter is less biased than a ridge estimator, and over much of the parameter space the pre-test estimator has smaller mean squared error than least squares. A ridge estimator is found to be inferior to the pre-test estimator in terms of mean squared error in many situations, and at worst the latter estimator is only slightly less efficient than the former at commonly used significance levels.