A property of coefficients of variation for ordered bivariate log-normal variables

This paper considers the maximum and minimum of a pair of log-normal variables with equal mean. It shows that either order statistic has a smaller coefficient of variation than the two original log-normal variables provided the latter are of equal variance. When the variances are unequal, as the variance ratio increases, the minimum (maximum), has a smaller coefficient of variation if the correlation coefficient of the log-normal variables is small (small) and the variances are large (small).     

Disabled Persons Support: The Serial Literature

Abstract

The authors anticipate that the primary use of this analysis will be to demonstrate journal price fluctuations by discipline and country of origin to department heads, university and hospital administrators, and others concerned (or who should be concerned) with the costs of health information. The authors expect to repeat this study on an annual basis and would welcome any comments.     

A simplified proof of a theorem on the difference of the moore–penrose inverses of two positive semi–difinte matrices

Simplified proofs are given of a standard result that establishes positive semi–definiteness of the difference of the inverses of two non–singular matrices, and of the extension of this result by Milliken and Akdeniz (1977) to the difference of the Moore–Penrose inverse of two singular matrices.     

The U.S. Health System and Immigration: An Institutional Interpretation1

We examine the institutions that comprise the U.S. health system and their relationship to a surging immigrant population. The clash between the system and this human flow originates in the large number of immigrants who are unauthorized, poor, and uninsured and, hence, unable to access a system largely based on ability to pay. Basic concepts from sociological theory are brought to bear on the analysis of this clash and its consequences. Data from a recently completed study of health institutions in three areas of the United States are used as an empirical basis to illustrate various aspects of this complex relation. Implications of our results for theory and future health policy are discussed.     

A Social Comparison Perspective of Treatment Seeking by the Homeless

The homeless will readily list ailments they claim to have, yet they will also claim that such ailments are not a problem and do not warrant treatmente In an attempt to understand this apparent paradox, data from a sampLe of homeless individuals were re-analyzed from a social comparison perspective. It was hypothesized that increased entrenchment in homelessness (increased isolation from “nonhomeless” environments and further involvement in the homeless environment) — operationalized by longer time spent on the street and a greater number of street friends — would lead homeless individuals to alter their perceptions of what constitutes a problem worthy of treatment. Results supported this hypothesis, in direction, and in almost all tests conducted. The involvement of adaptation level theory in such a social comparison process and the implications for intelVention and treatment are discussed.     

A Bias–Reduced Log–Periodogram Regression Estimator for the Long–Memory Parameter

In this paper, we propose a simple bias–reduced log–periodogram regression estimator, ^d_r, of the long–memory parameter, d, that eliminates the first– and higher–order biases of the Geweke and Porter–Hudak (1983) (GPH) estimator. The bias–reduced estimator is the same as the GPH estimator except that one includes frequencies to the power 2k for k=1,…,r, for some positive integer r, as additional regressors in the pseudo–regression model that yields the GPH estimator. The reduction in bias is obtained using assumptions on the spectrum only in a neighborhood of the zero frequency. Following the work of Robinson (1995b) and Hurvich, Deo, and Brodsky (1998), we establish the asymptotic bias, variance, and mean–squared error (MSE) of ^d_r, determine the asymptotic MSE optimal choice of the number of frequencies, m, to include in the regression, and establish the asymptotic normality of ^d_r. These results show that the bias of ^d_r goes to zero at a faster rate than that of the GPH estimator when the normalized spectrum at zero is sufficiently smooth, but that its variance only is increased by a multiplicative constant. We show that the bias–reduced estimator ^d_r attains the optimal rate of convergence for a class of spectral densities that includes those that are smooth of order s≥1 at zero when r≥(s−2)/2 and m is chosen appropriately. For s>2, the GPH estimator does not attain this rate. The proof uses results of Giraitis, Robinson, and Samarov (1997). We specify a data–dependent plug–in method for selecting the number of frequencies m to minimize asymptotic MSE for a given value of r. Some Monte Carlo simulation results for stationary Gaussian ARFIMA (1, d, 1) and (2, d, 0) models show that the bias–reduced estimators perform well relative to the standard log–periodogram regression estimator.     

Optimal Planning Quantities for Product Transition

The replacement of an existing product with a new one presents many challenges. In particular, uncertainties in a new product introduction often lead to extreme cases of demand and supply mismatches. This paper addresses inventory planning decisions for product upgrades when there is no replenishment opportunity during the transition period. We allow product substitution: when a company runs out of the old product, a customer may be offered the new product as a substitute. We show that the optimal substitution decision is a time‐varying threshold policy and establish the optimal planning policy. Further, we determine the optimal delay in a new product introduction, given the initial inventory of the old product.