Using Discrete-Time Event History Fertility Models to Simulate Total Fertility Rates and Other Fertility Measures

Event history models, also known as hazard models, are commonly used in analyses of fertility. One drawback of event history models is that the conditional probabilities (hazards) estimated by event history models do not readily translate into summary measures, particularly for models of repeatable events, like childbirth. In this paper, we describe how to translate the results of discrete-time event history models of all births into well-known summary fertility measures: simulated age- and parity-specific fertility rates, parity progression ratios, and the total fertility rate. The method incorporates all birth intervals, but permits the hazard functions to vary across parities. It can also simulate values for groups defined by both fixed and time-varying covariates, such as marital or employment life histories. We demonstrate the method using an example from the National Survey of Family Growth and provide an accompanying data file and Stata program.     

On the Measurement of Interpersonal Trust Transfer: Proposal of Indexes

This paper investigates the measurement of trust transfer in interpersonal contexts. Despite the importance of this topic, little evidence of comparable indicators exists to fill the current gap in knowledge regarding the measurement of interpersonal transference of trust and its reciprocal. We propose a modification of the classical structure of a widely accepted experimental setting for analyzing trust (i.e. a trust game) and build two indexes for the measurement of trust transfer and its reciprocation. Several properties of these indicators are investigated to examine their robustness and accuracy for measuring the transference of interpersonal trust. Furthermore, using data from our own modified trust game, we provide an empirical examination of both indexes. The results show that the existence of a mutually known third party fosters a stronger willingness to achieve cooperative behavior in an anonymous trust game; thus, the transference of interpersonal trust works in both directions of the dyad.     

Dublin’s Seventeenth Century Parishes and the Quality of Life

Social Indicators Research - The registers of Dublin’s parishes in the seventeenth century provide access to aspects of civic and religious life. In the registers are records of burials,...     

The Longitudinal Relationships Between Basic Psychological Needs Satisfaction at School and School-Related Subjective Well-Being in Adolescents

Situated within a positive psychology perspective, this study aimed at examining the nature and directionality of longitudinal relationships between basic psychological needs satisfaction at school and adolescents’ school-related subjective well-being. A total of 576 students (40.5 male and 36.8 % students in junior high school) completed measures of adolescent students’ basic psychological needs at school and school-related subjective well-being at two time points, 6 weeks apart. Cross-lagged structural equation modeling showed significant bidirectional longitudinal relationships between autonomy, relatedness, and competence needs satisfaction and school satisfaction. Furthermore, significant bidirectional relationships were observed between competence need satisfaction and positive affect in school. The findings provided important evidence of the roles of adolescents’ different types of needs satisfaction, experienced specifically during school, in adolescents’ school-related subjective well-being. The findings also helped extend the positive psychology perspective to the relatively neglected context of education.     

Asymptotic theory for maximum likelihood estimates in reduced-rank multivariate generalized linear models

Reduced-rank regression is a dimensionality reduction method with many applications. The asymptotic theory for reduced rank estimators of parameter matrices in multivariate linear models has been studied extensively. In contrast, few theoretical results are available for reduced-rank multivariate generalized linear models. We develop M-estimation theory for concave criterion functions that are maximized over parameter spaces that are neither convex nor closed. These results are used to derive the consistency and asymptotic distribution of maximum likelihood estimators in reduced-rank multivariate generalized linear models, when the response and predictor vectors have a joint distribution. We illustrate our results in a real data classification problem with binary covariates.     

A new algorithm for clustering based on kernel density estimation

In this paper, we present an algorithm for clustering based on univariate kernel density estimation, named ClusterKDE. It consists of an iterative procedure that in each step a new cluster is obtained by minimizing a smooth kernel function. Although in our applications we have used the univariate Gaussian kernel, any smooth kernel function can be used. The proposed algorithm has the advantage of not requiring a priori the number of cluster. Furthermore, the ClusterKDE algorithm is very simple, easy to implement, well-defined and stops in a finite number of steps, namely, it always converges independently of the initial point. We also illustrate our findings by numerical experiments which are obtained when our algorithm is implemented in the software Matlab and applied to practical applications. The results indicate that the ClusterKDE algorithm is competitive and fast when compared with the well-known Clusterdata and K-means algorithms, used by Matlab to clustering data.     

A Starting Point for Navigating the World of Daily Fantasy Basketball

Fantasy sports, particularly the daily variety in which new lineups are selected each day, are a rapidly growing industry. The two largest companies in the daily fantasy business, DraftKings and Fanduel, have been valued as high as $2 billion. This research focuses on the development of a complete system for daily fantasy basketball, including both the prediction of player performance and the construction of a team. First, a Bayesian random effects model is used to predict an aggregate measure of daily NBA player performance. The predictions are then used to construct teams under the constraints of the game, typically related to a fictional salary cap and player positions. Permutation based and K-nearest neighbors approaches are compared in terms of the identification of “successful” teams—those who would be competitive more often than not based on historical data. We demonstrate the efficacy of our system by comparing our predictions to those from a well-known analytics website, and by simulating daily competitions over the course of the 2015–2016 season. Our results show an expected profit of approximately $9,000 on an initial $500 investment using the K-nearest neighbors approach, a 36% increase relative to using the permutation-based approach alone. Supplementary materials for this article are available online.