Uniform convergence of an empirical cdf based on a relatively small number of order statistics

An empirical distribution function F_m, defined on a subset of order statistics of a random sample of size n taken from the distribution of a random variable with continuous distribution function F, is shown to converge uniformly with probability one to F. Small sample distributions of the one and two sided deviations and the asymptotic normality of the standardized F_m are established. The relative efficiency of F_m as compared to the classical empirical distribution function is calculated and tabled. for n = 10, 20, 50, 100, 200.     

The Role of Beliefs in Sexual Behavior of Adolescents: Development and Validation of an Adolescent Sexual Expectancies Scale

This article reports on the development and psychometric properties of the Adolescent Sexual Expectancies Scale (ASEXS). Data were obtained from three annual longitudinal surveys of youth aged 10–17 at the first administration (N=932 at Wave 3). Confirmatory factor analyses indicated that 4 correlated factors corresponding to Social Risk, Social Benefit, Health Risk, and Pleasure adequately represented the expectancy items. High alpha coefficients and stability across survey waves indicated good internal and test–retest reliability for these factors. Latent variable autoregressive analyses indicated that Social Risk expectancies predicted changes in sexual behavior over the course of the study. Multigroup analyses established that the measurement and predictive models were equivalent for males and females and across age groups. The ASEXS provides researchers with a useful measure for examining adolescents' expectancy beliefs about the consequences of sexual behavior.     

Conditional Score Tests for Heteroscedasticity in the Two-Way Error Components Model

This paper extends the one-way heteroskedasticity score test of Holly and Gardiol (2000, In: Krishnakumar, J, Ronchetti, E (Eds.), Panel Data Econometrics: Future Directions, North-Holland, Amsterdam, pp. 199–211) to two conditional Lagrange Multiplier (LM) tests of heteroskedasticity under contiguous alternatives within the two-way error components model framework. In each case, the derivation of Rao's efficient score statistics for testing heteroskedasticity is first obtained. Then, based on a specific set of assumptions, the asymptotic distribution of the score under contiguous alternatives is established. Finally, the expression for the score test statistic in the presence of heteroskedasticity and related asymptotic local powers of these score test statistics are derived and discussed.     

Markov's Inequality and Chebyshev's Inequality for Tail Probabilities: A Sharper Image

Markov's inequality gives an upper bound on the probability that a nonnegative random variable takes large values. For example, if the random variable is the lifetime of a person or a machine, Markov's inequality says that the probability that an individual survives more than three times the average lifetime in the population of such individuals cannot exceed one-third. Here we give a simple, intuitive geometric interpretation and derivation of Markov's inequality. These results lead to inequalities sharper than Markov's when information about conditional expectations is available, as in reliability theory, demography, and actuarial mathematics. We use these results to sharpen Chebyshev's tail inequality also.     

Institutional Academic Industry Relationship: Results of Interviews with University Leaders

The purpose of this study was to explore qualitatively the nature, consequences and management of institutional academic industry relationships (IAIRs) in the life and health sciences. The results of our interviews suggest that in the institutions we visited IAIRs are common and deeply embedded in the academic enterprise both among institutional officials and the organization itself. Many believe these relationships have potential benefits for the research and education mission while creating the potential for conflicts of interest. Further tracking and study of IAIRs is required to understand and anticipate the full effects of these relationships on the scientific enterprise in the United States.     

Social problems clusters as contexts for claimsmaking: implications for the study of off-campus housing

ABSTRACT

Many social problems claims occur in a context that involves (1) actors’ recognition of other stakeholders, (2) relationships among those actors, and (3) varying temporal frameworks that influence how each claimsmaker views the current collection of issues. We call actors’ involvement in overlapping issues a social problems cluster, a set of claimsmaking efforts that involve many of the same people and groups as advocates, or opponents. Using a case study of local student housing issues as an example, we suggest the following regarding the social problems cluster. First, we consider how the interactions among social problems cluster members shape what happens with a particular issue. Second, we consider the way participants within one social problems cluster recognize links may reflect participants’ biographies, interests, ideologies, and so forth. Attending to how the social problems cluster’s members interact and link issues helps to locate a particular claim within its broader context.     

NEW DOMAINS FOR STRUCTURAL ANALYSIS

Can network thinking be extended to the broad range of social organization conventionally studies by sociologists? Applying structural thinking to American election data, it is possible to detect a social-economic hierarchy solely from the links among categories, without reference to external numerical indicators of rank. It is possible to detect and verify sensible and suggestive relative social positions for categories of income, education, gender, age, religion, race, party identification, and region – forming correlated hierarchies. The special contribution of the structural analysis is its simultaneous attention to detail and the overview of links, coupled with less dependence on a priori assumptions.     

Book reviews

Clinical Social Work. Helen Northen. (New York: Columbia University Press, 1982.)

The Fifty‐Minute Hour: A Collection of True Psychoanalytic Tales. Robert Lindner. (New York and London: Jason Aronson, 1982.)

Clinical Work With Children. Judith M. Mishne. (New York: The Free Press, 1983.)

The Father Figure. Lorna McKee and Margaret O'Brien, Editors. (London: Tavistock Publications, 1982.)     

Applied Nonparametric Instrumental Variables Estimation

Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used to identify and estimate these parameters. However, linear and other finite‐dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators.