Methods For Comparing The Mortality Experience of Heterogeneous Populations

Methods are presented which produce Maximum Likelihood Estimates (MLE) of the degree of heterogeneity in individual mortality risks under a variety of assumptions about the age trajectory of those mortality risks. With these estimates of the degree of population heterogeneity it is possible to adjust comparisons of mortality risks across populations for the effects of population heterogeneity, differential mortality selection, and different age trajectories of the force of mortality. These methods are demonstrated by applying a variety of standard assumptions about the age trajectory of the force of mortality to the analysis of a broad range of cohort mortality data for the U.S. and Swedish populations. The estimates of the degree of heterogeneity, produced under all of the selected force of mortality models, consistently indicated a considerable degree of heterogeneity in mortality risks.     

No sustained effect of aerobic or resistance training on insulin sensitivity in nonobese, healthy older women

It is unclear whether long-term aerobic (AT) or resistance (RT) training can improve insulin sensitivity (IS) beyond the residual effect of the last training bout in older women (54-78 years). Therefore, a group of nonobese, healthy older women underwent 6 months of AT (n = 8) or RT (n = 10), and the authors measured IS 4 days after the last training bouts using the hyperinsulinemic-euglycemic clamp technique. Women trained 3 days/week. AT consisted of 25- to 60-min sessions of walking/jogging at 60-95% of maximal heart rate. RT consisted of three sets of nine exercises repeated 10 times at 80% of 1 repetition maximum. AT decreased fat mass, whereas both AT and RT increased fat-free mass. Neither training program, however, improved absolute or relative rates of glucose disposal. The authors therefore concluded that nonobese, healthy older women should perform AT or RT on a daily basis in order to improve IS and maintain the improvement.     

Immigration,Ethnicity, and Conflict: The California Chinese, 1849–1882*

Drawing on Bonacich's split market theory, the work of Hechter and Blalock on ethnic conflict, and the literature on social movements, the authors develop a model of factors producing conflict between native and immigrant workers. The model identifies the relative size of the immigrant group, the growth of the immigrant population, the desirability of jobs held by immigrant workers, economic conditions, the development of racist ideology among native workers, and the organization of native workers as factors possibly explaining the frequency of incidents of overt ethnic conflict and efforts to institutionalize discrimination through government action. The model is assessed by examining the situation of Chinese immigrants in California between 1849 and 1882. The data suggest that poor economic conditions, a well-developed racist ideology, and well-organized native workers best explain incidents of ethnic conflict and successful efforts to obtain discriminatory government action.     

Anthropological versus sociological approaches to the study of soccer hooliganism: some critical notes

It is argued that the anthropological approach, as used by Armstrong and Harris, has not generated any breakthrough in the study of soccer hooliganism. In particular, it is suggested that their use of a commonsense rather than a sociological concept of violence vitiates their analysis in several ways, contributing above all to substantial inconsistencies between some of their own empirical data and their general conclusions concerning levels of soccer-related violence. It is also contended that their critique of the ‘figurational’ or ‘process-sociological’ approach followed by the Leicester researchers is based on a confused misrepresentation of that approach. Specifically it is argued (i) that Armstrong and Harris fail to recognize the wide range of methods, including extensive participant observation, used by the Leicester group, (ii) that their attempt to cast doubt on the Leicester group's contention that the core football hooligans come predominantly from the ‘rougher’ sections of the working class is based on nothing more than a priori speculation. In this connection, Armstrong and Harris themselves provide no reliable data on the social class of soccer hooligans in Sheffield, and they seem unaware of the fact that several different sources of data appear to confirm the finding of the Leicester group, (iii) they have misunderstood both the terminology and the reasoning of the Leicester group concerning the ‘rougher’ sections of the working class and their relationship to football hooliganism.     

Supportive parenting moderates the effect of discrimination upon anger, hostile view of relationships, and violence among African American boys

Studies have shown that exposure to discrimination increases the probability that African American adolescents will engage in delinquent behavior, especially acts of violence. The present study extended this research by examining the extent to which supportive parenting buffers a youth from these deleterious consequences of discrimination. Analyses based upon two waves of data from a sample of 332 African American adolescent males and their caretakers supported this hypothesis. Further the results indicated that there are two avenues whereby supportive parenting reduces the probability that discrimination will lead to violence. First, supportive parenting decreases the chances that discrimination will lead to anger and a hostile view of relationships. Second, supportive parenting lowers the risk that anger or a hostile view of relationships, when they develop, will result in violence.     

A Bayesian Time Series Model of Multiple Structural Changes in Level,Trend, and Variance

We investigate whether seasonal-adjustment procedures are, at least approximately, linear data transformations. This question was initially addressed by Young and is important with respect to many issues including estimation of regression models with seasonally adjusted data. We focus on the X-11 program and rely on simulation evidence, involving linear unobserved component autoregressive integrated moving average models. We define a set of properties for the adequacy of a linear approximation to a seasonal-adjustment filter. These properties are examined through statistical tests. Next, we study the effect of X-11 seasonal adjustment on regression statistics assessing the statistical significance of the relationship between economic variables. Several empirical results involving economic data are also reported.     

A bayesian analysis of trend determination in economic time series

In this paper we provide a comprehensive Bayesian posterior analysis of trend determination in general autoregressive models. Multiple lag autoregressive models with fitted drifts and time trends as well as models that allow for certain types of structural change in the deterministic components are considered. We utilize a modified information matrix-based prior that accommodates stochastic nonstationarity, takes into account the interactions between long-run and short-run dynamics and controls the degree of stochastic nonstationarity permitted. We derive analytic posterior densities for all of the trend determining parameters via the Laplace approximation to multivariate integrals. We also address the sampling properties of our posteriors under alternative data generating processes by simulation methods. We apply our Bayesian techniques to the Nelson-Plosser macroeconomic data and various stock price and dividend data. Contrary to DeJong and Whiteman (1989a,b,c), we do not find that the data overwhelmingly favor the existence of deterministic trends over stochastic trends. In addition, we find evidence supporting Perron's (1989) view that some of the Nelson and Plosser data are best construed as trend stationary with a change in the trend function occurring at 1929.     

Nonparametric Regression with Sample Design Following a Random Process

Nonparametric regression is considered where the sample point placement is not fixed and equispaced, but generated by a random process with rate n. Conditions are found for the random processes that result in optimal rates of convergence for nonparametric regression when using a block thresholded wavelet estimator. Previous results on nonparametric regression via wavelets on both fixed and random sample point placement are shown to be special cases of the general result given here. The estimator is adaptive over a large range of Hölder function spaces and the convergence rate exhibited is an improvement over term-by-term wavelet estimators. Threshold selection is implemented in a data-adaptive fashion, rather than using a fixed threshold as is usually done in block thresholding. This estimator, BlockSure, is compared against fixed-threshold block estimators and the more traditional term-by-term threshold wavelet estimators on several random design schemes via simulations.