Marginal and association regression models for longitudinal binary data with drop‐outs: A likelihood‐based approach

Longitudinal data often contain missing observations, and it is in general difficult to justify particular missing data mechanisms, whether random or not, that may be hard to distinguish. The authors describe a likelihood‐based approach to estimating both the mean response and association parameters for longitudinal binary data with drop‐outs. They specify marginal and dependence structures as regression models which link the responses to the covariates. They illustrate their approach using a data set from the Waterloo Smoking Prevention Project They also report the results of simulation studies carried out to assess the performance of their technique under various circumstances.     

Becoming American/Becoming New Yorkers: Immigrant Incorporation in a Majority Minority City1

Many observers have noted that immigrants to the United States are highly concentrated in the largest metropolitan areas of a relatively few states. Though immigrants diffused into many places that had previously seen relatively few immigrants during the 1990s, as of the 2000 census, 77 percent of the nation's 31.1 million foreign born residents still lived in six states — California, New York, Texas, Florida, New Jersey, and Illinois. According to the 2000 census, the two largest metropolitan areas, Los Angeles and New York, accounted for one third of all immigrants ( http://www.census.gov/Press‐Release/www/2002/demoprofiles.html ). While immigrants moved into many new areas during the 1990s, making the challenge of incorporating their children a national issue, their concentration in our largest cities remained pronounced.     

Gay Affirmative Practice: A Model for Social Work Practice with Gay,Lesbian, and Bisexual Youth

Gay affirmative practice has recently been introduced into the social work literature as a culturally sensitive model for working with gay, lesbian, and bisexual (GLB) adults, however, this model has rarely been applied to practice with GLB youth. In this article, the authors review the literature to present the main tenets of gay affirmative practice, outline the challenges that GLB youth face, and delineate the environmental and individual strengths that can be enhanced to promote well-being. The authors then apply the gay affirmative practice model to GLB youth, offering concrete information about the specific knowledge, attitudes, and skills that social workers should acquire to better serve the unique, yet diverse, needs of GLB youth.     

Successful collaboration between the nonprofit and public sectors

Public and private funding sources often require nonprofit organizations to provide evidence of partnership with a governmental entity before financing a project. However, the circumstances under which working partnerships between the nonprofit and public sectors are forged and sustained have not been fully studied. This article presents the findings of a case study of land trusts and local governments and identifies conditions that foster successful collaboration. Social factors such as experience on the part of key personnel in working with the opposite entity and genuine affection for each other are more important than economic benefits. This suggests that a nonprofit agency interested in creating a viable partnership to improve a project should give careful consideration to assigning staff.     

Consistent maximum likelihood estimation of a unimodal density using shape restrictions

It is well known that the unimodal maximum likelihood estimator of a density is consistent everywhere but at the mode. The authors review various ways to solve this problem and propose a new estimator that is concave over an interval containing the mode; this interval may be chosen by the user or through an algorithm. The authors show how to implement their solution and compare it to other approaches through simulations. They show that the new estimator is consistent everywhere and determine its rate of convergence in the Hellinger metric.     

Conditional Likelihood Estimators for Hidden Markov Models and Stochastic Volatility Models

ABSTRACT.  This paper develops a new contrast process for parametric inference of general hidden Markov models, when the hidden chain has a non-compact state space. This contrast is based on the conditional likelihood approach, often used for ARCH-type models. We prove the strong consistency of the conditional likelihood estimators under appropriate conditions. The method is applied to the Kalman filter (for which this contrast and the exact likelihood lead to asymptotically equivalent estimators) and to the discretely observed stochastic volatility models.