Victim Experiences in Hate Crimes Based on Sexual Orientation

Using interview data from a convenience sample of 450 lesbian, gay, and bisexual adults, the varieties of victim experiences in hate crimes based on sexual orientation are described. Most crimes were perpetrated in public settings by one or more strangers, but victimization also occurred in other locales, and perpetrators included neighbors, coworkers, and relatives. In deciding whether a crime was based on their sexual orientation, victims tended to rely primarily on contextual cues and perpetrators' explicit statements. Victims' concerns about police bias and public disclosure of their sexual orientation were important factors in deciding whether to report antigay crimes, as were beliefs about the crime's severity and the likelihood that perpetrators would be punished.     

Tests for noncorrelation of two multivariate ARMA time series

In many situations, we want to verify the existence of a relationship between multivariate time series. In this paper, we generalize the procedure developed by Haugh (1976) for univariate time series in order to test the hypothesis of noncorrelation between two multivariate stationary ARMA series. The test statistics are based on residual cross-correlation matrices. Under the null hypothesis of noncorrelation, we show that an arbitrary vector of residual cross-correlations asymptotically follows the same distribution as the corresponding vector of cross-correlations between the two innovation series. From this result, it follows that the test statistics considered are asymptotically distributed as chi-square random variables. Two test procedures are described. The first one is based on the residual cross-correlation matrix at a particular lag, whilst the second one is based on a portmanteau type statistic that generalizes Haugh's statistic. We also discuss how the procedures for testing noncorrelation can be adapted to determine the directions of causality in the sense of Granger (1969) between the two series. An advantage of the proposed procedures is that their application does not require the estimation of a global model for the two series. The finite-sample properties of the statistics introduced were studied by simulation under the null hypothesis. It led to modified statistics whose upper quantiles are much better approximated by those of the corresponding chi-square distribution. Finally, the procedures developed are applied to two different sets of economic data.     

Undocumented migration to Venezuela

"In 1980 Venezuela took...steps to regularize the undocumented migrant population. While the number responding to the amnesty was small relative to expectations, the majority of illegals appeared to have regularized their status. For the first time it was possible to assess objectively the characteristics of the undocumented population. Moreover, the problem of illegal migrants seems to have been temporarily solved, a result of both the amnesty and the country's declining economic activity." Topics covered in the present article include the nationality, geographic distribution, sex and age distribution, educational status, and occupations of undocumented migrants.     

Strategic planning in a changing corporate environment

Strategic planning is about nothing, if it is not about attempting to achieve desirable futures. In the past this has often been seen as an excuse, or indeed a necessity, for fixed goals which are constantly timed. Such a state of planning has, fortunately, for a long time now been discredited and this paper concentrates upon the concepts of strategic planning within the changing corporate environment, an environment which therefore has to effect both the goals as well as the process of planning. Furthermore, the author concentrates upon the concept of a planning, allocating, and monitoring cycle of strategic planning which sets the strategic planning concept within a process cycle as well as within an effective allocative structure. The author considers this latter point an extremely important one since, as he says, ‘planning can become a very sterile and barren activity if it is not viewed integrally with acting and doing’. Finally, this paper concentrates on the important fact that planning must, within a dynamic environment, be an iterative and a learning process.     

Estimating the hazard functions of two alternating recurrent events in the presence of covariates

The motivation for this paper is a cystic fibrosis data which records a patient’s times to relapse and times to cure under several recurrences of the disease. The idea is to study the impact of covariates on the hazard rates of two alternately occurring events. The dependence between the times to the two events over the different cycles is modeled through an autoregressive-type setup. The partial likelihood function is then derived and the estimators obtained. The estimators are shown to be consistent and asymptotically normal. The technique is applied to study the motivating data. A simulation study is also conducted to corroborate the results.     

Computation aspects of the parameter estimates of linear mixed effects model in multivariate repeated measures set-up

The number of parameters mushrooms in a linear mixed effects (LME) model in the case of multivariate repeated measures data. Computation of these parameters is a real problem with the increase in the number of response variables or with the increase in the number of time points. The problem becomes more intricate and involved with the addition of additional random effects. A multivariate analysis is not possible in a small sample setting. We propose a method to estimate these many parameters in bits and pieces from baby models, by taking a subset of response variables at a time, and finally using these bits and pieces at the end to get the parameter estimates for the mother model, with all variables taken together. Applying this method one can calculate the fixed effects, the best linear unbiased predictions (BLUPs) for the random effects in the model, and also the BLUPs at each time of observation for each response variable, to monitor the effectiveness of the treatment for each subject. The proposed method is illustrated with an example of multiple response variables measured over multiple time points arising from a clinical trial in osteoporosis.