Sex, self-interest and health care priorities

In this questionnaire study, individuals were asked to prioritise publicly provided preventive health care services, one of which would be unavailable to them by virtue of their sex. The aim was to establish whether men and women would exhibit different degrees of self-interest when making a constrained choice. Around 1800 subjects from east-central England prioritised three different types of cancer screening. Most also provided written explanations for their rankings and these were classified into explanatory themes. Logistic regressions using socio-demographic and attitude data predicted the type of screening chosen as first priority. The analysis revealed that many men and women did indeed assign similar priorities to the different types of screening and, even when the priorities differed, these were often justified by similar arguments relating to technical aspects of the interventions and to self-interest. However, women were far more likely than men to prioritise a type of screening from which they themselves would benefit directly and the variations in preferences and explanations between the sexes occurred primarily because of differences in other-regarding attitudes. The bias towards screening of females was driven by women's greater worries about the disease in question and by men's “benevolent sexism” with respect to women's wellbeing.     

Mr.J.F.McLennan's Study of Polyandry

John F.McLennan is well known for introducing the terms of “exogamy” and “en-dogamy” in Primitive Marriage:An Inquiry into the Origins of the Form of Capture in Marriage Ceremo-nies(1865) .In this book...     

Noninformative priors for linear combinations of normal means with unequal variances

For normal populations with unequal variances, we develop matching priors and reference priors for a linear combination of the means. Here, we find three second-order matching priors: a highest posterior density (HPD) matching prior, a cumulative distribution function (CDF) matching prior, and a likelihood ratio (LR) matching prior. Furthermore, we show that the reference priors are all first-order matching priors, but that they do not satisfy the second-order matching criterion that establishes the symmetry and the unimodality of the posterior under the developed priors. The results of a simulation indicate that the second-order matching prior outperforms the reference priors in terms of matching the target coverage probabilities, in a frequentist sense. Finally, we compare the Bayesian credible intervals based on the developed priors with the confidence intervals derived from real data.     

Modified minimax estimation of regression coefficients

Working with the linear regression model (1.1) and having the extraneous information (1.2) about regression coefficients the problem exists how to build estimators (1.3) with the risk (1.4) which enable to utilize the known information in order to reduce their risk as compared with the risk (1.6) of the LSE (1.5). Solution of this problem is known for the positive definite matrix T, namely in form for estimators (1.8) and (1.10).First, it is shown that the proposed estimators (2.6),(2.9) and (2.16) based on psedoinversions of the matrix L represent the solution of the problem of the positive semidefinite matrix T=L'L.Further, the problem of interpretability of estimators in the sense of the inequality (3.1) exists; it is shown that all mentioned estimators are at least partially interpretable in the sense of requirements (3.2) or (3.10).     

Methods for calculating stationary distribution in linear models of time series

The article deals with methods for computing the stationary marginal distribution in linear models of time series. Two approaches are described. First, an algorithm based on approximation of solution of the corresponding integral equation is briefly reviewed. Then, we study the limit behaviour of the partial sums c ₁ η₁+c ₂ η₂+···+c _n η _n where η _i are i.i.d. random variables and c _i real constants. We generalize procedure of Haiman (1998) [Haiman, G., 1998, Upper and lower bounds for the tail of the invariant distribution of some AR(1) processes. Asymptotic Methods in Probability and Statistics, 45, 723–730.] to an arbitrary causal linear process and relax the assumptions of his result significantly. This is achieved by investigating the properties of convolution of densities.     

Banking on fewer children: financial intermediation,fertility and economic development

This paper shows that financial intermediation can influence fertility and labor allocation decisions by raising market wages. The increase in wages induces some households to abandon “traditional” labor intensive methods of production managed at the household level and supply labor to “modern” sector firms. Since it is optimal for households in the modern sector to have fewer children, the labor allocation decision leads to lower national fertility. A panel VAR using financial intermediation, fertility and industrial employment share data in 87 countries is estimated. The empirical results show that the data are consistent with the theoretical predictions. Received: 20 October 1997/Accepted: 31 August 1998     

Quadratic estimation in mixed linear models with two variance components

Explicit expressions for Bayes invariant quadratic estimates, biased and unbiased, are presented and proved to cover the entire class of admissible estimates in the considered classes. An unbalanced genetic model is studied for demonstration.     

Asymptotic properties of a generalized regression-type predictor of a finite population variance in probability sampling

A system of predictors for estimating a finite population variance is defined and shown to be asymptotically design-unbiased (ADU) and asymptotically design-consistent (ADC) under probability sampling. An asymptotic mean squared error (MSE) of a generalized regression-type predictor, generated from the system, is obtained. The suggested predictor attains the minimum expected variance of any design-unbiased estimator when the superpopulation model is correct. The generalized regression-type predictor and the predictor suggested by Mukhopadhyay (1990) are compared.