Assessing reoffense risk with juvenile sexual offenders

This article summarizes a two-year study of juvenile sexual offenders in Washington. Both community-based and institution-based treatment programs were evaluated. A typical profile of the juvenile sexual offender is offered, as well as recidivism data from a mean 20-month follow-up period.     

Semiparametric sieve maximum likelihood estimation under cure model with partly interval censored and left truncated data for application to spontaneous abortion

Lifetime Data Analysis - This work was motivated by observational studies in pregnancy with spontaneous abortion (SAB) as outcome. Clearly some women experience the SAB event but the rest do not....     

Money-metric utilitarianism

We discuss a method of ranking allocations in economic environments which applies when we do not know the names or preferences of individual agents. We require that two allocations can be ranked with the knowledge only of agents present, their aggregate bundles, and community indifference sets—a condition we refer to as aggregate independence. We also postulate a basic Pareto and continuity property, and a property stating that when two disjoint economies and allocations are put together, the ranking in the large economy should be consistent with the rankings in the two smaller economies (reinforcement). We show that a ranking method satisfies these axioms if and only if there is a probability measure over the strictly positive prices for which the rule ranks allocations on the basis of the random-price money-metric utilitarian rule. This is a rule which computes the money-metric utility for each agent at each price, sums these, and then takes an expectation according to the probability measure.     

“Why I Can't Stand Out in Front of My House?”: Street‐Identified Black Youth and Young Adult's Negative Encounters With Police

This street participatory action research (Street PAR) study organized 15 residents to document street‐identified Black youth and adult's negative experiences with police in Wilmington, Delaware. Data were collected on mostly street‐identified Black men and women aged 18–35 in the forms of (1) 520 surveys, (2) 24 individual interviews, (3) four dual interviews, (4) three group interviews, and (5) extensive field observations. Forty‐two percent of survey participants reported being stopped by police in the last year. However, with the exception of being “stopped,” participants overall reported little negative contact with police at least within the past year. Chi‐square and ANOVA analyses suggest an interactional relationship exists between race, gender, and age on experiences with police. Younger Black men (18–21) were found to have the most negative contact with police. Analysis suggests a smaller, more hardened mostly male variant of the larger street community has had repeated contact with police. Qualitative analysis reveals at least two major themes: (1) disrespect and disdain for residents and (2) low motivation for working with police. Street PAR methodology was also found to be instrumental in working with local residents and the Wilmington Police Department to improve conditions between residents and police.     

M-quantile models with application to poverty mapping

Over the last decade there has been growing demand for estimates of population characteristics at small area level. Unfortunately, cost constraints in the design of sample surveys lead to small sample sizes within these areas and as a result direct estimation, using only the survey data, is inappropriate since it yields estimates with unacceptable levels of precision. Small area models are designed to tackle the small sample size problem. The most popular class of models for small area estimation is random effects models that include random area effects to account for between area variations. However, such models also depend on strong distributional assumptions, require a formal specification of the random part of the model and do not easily allow for outlier robust inference. An alternative approach to small area estimation that is based on the use of M-quantile models was recently proposed by Chambers and Tzavidis (Biometrika 93(2):255–268, 2006) and Tzavidis and Chambers (Robust prediction of small area means and distributions. Working paper, 2007). Unlike traditional random effects models, M-quantile models do not depend on strong distributional assumption and automatically provide outlier robust inference. In this paper we illustrate for the first time how M-quantile models can be practically employed for deriving small area estimates of poverty and inequality. The methodology we propose improves the traditional poverty mapping methods in the following ways: (a) it enables the estimation of the distribution function of the study variable within the small area of interest both under an M-quantile and a random effects model, (b) it provides analytical, instead of empirical, estimation of the mean squared error of the M-quantile small area mean estimates and (c) it employs a robust to outliers estimation method. The methodology is applied to data from the 2002 Living Standards Measurement Survey (LSMS) in Albania for estimating (a) district level estimates of the incidence of poverty in Albania, (b) district level inequality measures and (c) the distribution function of household per-capita consumption expenditure in each district. Small area estimates of poverty and inequality show that the poorest Albanian districts are in the mountainous regions (north and north east) with the wealthiest districts, which are also linked with high levels of inequality, in the coastal (south west) and southern part of country. We discuss the practical advantages of our methodology and note the consistency of our results with results from previous studies. We further demonstrate the usefulness of the M-quantile estimation framework through design-based simulations based on two realistic survey data sets containing small area information and show that the M-quantile approach may be preferable when the aim is to estimate the small area distribution function.     

A methodological strategy for a one-number census in the UK

As a result of lessons learnt from the 1991 census, a research programme was set up to seek improvements in census methodology. Underenumeration has been placed top of the agenda in this programme, and every effort is being made to achieve as high a coverage as possible in the 2001 census. In recognition, however, that 100% coverage will never be achieved, the one-number census (ONC) project was established to measure the degree of underenumeration in the 2001 census and, if possible, to adjust fully the outputs from the census for that undercount. A key component of this adjustment process is a census coverage survey (CCS). This paper presents an overview of the ONC project, focusing on the design and analysis methodology for the CCS. It also presents results that allow the reader to evaluate the robustness of this methodology.     

ROBUST ESTIMATION OF SMALL‐AREA MEANS AND QUANTILES

Small‐area estimation techniques have typically relied on plug‐in estimation based on models containing random area effects. More recently, regression M‐quantiles have been suggested for this purpose, thus avoiding conventional Gaussian assumptions, as well as problems associated with the specification of random effects. However, the plug‐in M‐quantile estimator for the small‐area mean can be shown to be the expected value of this mean with respect to a generally biased estimator of the small‐area cumulative distribution function of the characteristic of interest. To correct this problem, we propose a general framework for robust small‐area estimation, based on representing a small‐area estimator as a functional of a predictor of this small‐area cumulative distribution function. Key advantages of this framework are that it naturally leads to integrated estimation of small‐area means and quantiles and is not restricted to M‐quantile models. We also discuss mean squared error estimation for the resulting estimators, and demonstrate the advantages of our approach through model‐based and design‐based simulations, with the latter using economic data collected in an Australian farm survey.     

The impact of involuntary job loss on those disabled by society: a pilot study to encourage effective participation

Over half of the people who become disabled whilst employed are thought to be pressured into leaving their job. This study investigates the experience of involuntary retirement/redundancy due to disability. Three researchers who had all experienced such involuntary job loss were trained to conduct the research. Semi-structured interviews were designed and conducted by disabled researchers. The training of the researchers enabled them to successfully conduct a research project. The results of the project show the impact of involuntary job loss due to disability to be considerable. This study would suggest that government reforms to create new opportunities for disabled people of a working age are well overdue. Although there were some problems experienced with the amount of training required and the lack of objectivity, training disabled researchers to conduct research proved to be a valuable experience for both the researchers and the facilitators.     

ESTIMATING DISTRIBUTION FUNCTIONS FROM SURVEY DATA WITH LIMITED BENCHMARK INFORMATION

The model-based approach to estimation of finite population distribution functions introduced in Chambers & Dunstan (1986) is extended to the case where only summary information is available for the auxiliary size variable. Monte Carlo results indicate that this ‘limited information’ extension is almost as efficient as the ‘full information’ method proposed in the above reference. These results also indicate that the model-based confidence intervals generated by either of these methods have superior coverage properties to more conventional design-based confidence intervals.