Do different dimensions of ethnic identity reduce the risk of violence among Asian American and Pacific Islander adolescents in Hawai‘i?

This study examines whether different dimensions of ethnic identity are associated with reduced risk of violence among an understudied population: Asian American and Pacific Islander adolescents. Drawing from survey data of 298 Native Hawaiian, Samoan, Japanese, and Filipino adolescents in Hawai‘i, this study focuses on whether three dimensions of ethnic identity (“affirmation and belonging,” “ethnic identity achievement,” and “other-group orientation”) related to overall and different types of violence. Results of the multivariate analyses reveal that higher levels of ethnic identity achievement are associated with reduced risk of overall violence and family/partner violence, which suggests that this dimension is a beneficial social-psychological resource. Contrary to our expectations, higher levels of ethnic affirmation and belonging are associated with attacking someone. In addition, other-group orientation has no relationship with violence. Implications of our findings regarding distinct effects for different dimensions of ethnic identification are discussed pertaining to developmental research and violence prevention.     

The Role of the Statistician: Scientist or Shoe Clerk

A carnival game is described in which a player rolls eight dice or eight marbles into holes. To win, the player must obtain a favorable outcome on a number of plays. The probability distribution for the outcome of a single play is discussed. The distribution of a winning combination of outcomes is discussed and approximated. These probabilities show that the game is essentially unbeatable.     

Bayesian analysis of reliability in multicomponent stress-strength models

This paper provides a Bayesian treatment of the problem of inference about the reliability of a multicomponent stress-strength system which functions if s or more of k identical components simultaneously operate. All stresses and strengths are assumed to be independent, exponentially distributed random variables. Exact and approximate asymptotic posterior distributions for the reliability are derived, and the results are illustrated by a numerical example.     

Bayesian assessment of assumptions of regression analysis

Many statistical procedures are based on the models which specify the conditions under which the data are generated. Many applications of linear regression, for example, assume that:(i) the observations are independent; (ii) the errors in the observations are identically distributed; (iii) each error has a normal distribution with mean zero and unknown variance σ²> 0. Previous works have examined individual departures from these assumptions. Here we examine composite departures. It is assumed that the error distribution in a linear model is power-exponential and that the observations are generated via a first order autoregressive model with the possibility of spurious observations. The consequences are illustrated via an example.     

“Your Picture Looks the Same as My Picture”: An Examination of Passing in Transgender Communities

Transgender people remain one of the groups most susceptible to discrimination in the U.S. Previous studies have examined the discrimination and stress transgender people face, but few studies have examined trans identities using existing sociological theories of marginalized groups and identity formation. Using the theories of Dubois and Cooley, this study explores identity formation in conjunction with the phenomenon of passing among transgender people residing in Nebraska. Results suggest that while trans people do pass as a mechanism for subverting discrimination, there are other factors that influence an individual’s choice and strategy to pass or not. The current investigation lends a sociological perspective on the social aspect of gender presentation and gender visibility for trans individuals in a largely conservative Midwestern state.     

Questions & Answers: The Role of Mathematical Models in Clinical Research

Given any generalized inverse (X'X)^? appropriate to normal equations X'Xb ⁰ = X'y for the linear model y = Xb + e, a procedure is given for obtaining from it a generalized inverse appropriate to a restricted model having restrictions P'b = 0 for P'b nonestimable.     

Procedures for investigating outliers when estimating in the general univariate linear situation-nonfull rank case

This paper is a continuation of GGD = Golub, Guttman and Dutter (1973) and DG = Dutter and Guttman (1974a), and will deal with the problem of how to handle outliers in the general univariate linear model situation y = Xθ + ?, ? = N(0, σ²I).when X is not of full rank, and where interest is in the estimation of τ = Bθ, where τ is assumed to be estimable. The special case B = X, that is, interest is in η = Xθ, is discussed.     

A bayesian approach to prediction in analysis of variance settings

This paper takes the results of Lindley and smith ( 1972 ) one step further, by finding the predictive distribution of an observation y^* whose distribution is normal, and centred at A^* ₁θ₁ We then apply this distribution to the case of prediction based on data obtained in one and two wau ANOVA situations. For Example, it turns out that for two way ANOVA with interaction, the predictive mean, (which we would use as the predictor) is a weighted combination of sample main effects and interaction effects