Modified quasi-profile likelihoods from estimating functions

We discuss higher-order adjustments for a quasi-profile likelihood for a scalar parameter of interest, in order to alleviate some of the problems inherent to the presence of nuisance parameters, such as bias and inconsistency. Indeed, quasi-profile score functions for the parameter of interest have bias of order O(1)$\mathrm{O}(1)$, and such bias can lead to poor inference on the parameter of interest. The higher-order adjustments are obtained so that the adjusted quasi-profile score estimating function is unbiased and its variance is the negative expected derivative matrix of the adjusted profile estimating equation. The modified quasi-profile likelihood is then obtained as the integral of the adjusted profile estimating function. We discuss two methods for the computation of the modified quasi-profile likelihoods: a bootstrap simulation method and a first-order asymptotic expression, which can be simplified under an orthogonality assumption. Examples in the context of generalized linear models and of robust inference are provided, showing that the use of a modified quasi-profile likelihood ratio statistic may lead to coverage probabilities more accurate than those pertaining to first-order Wald-type confidence intervals.     

Analysis of growth curve data by using cubic smoothing splines

Longitudinal data frequently arises in various fields of applied sciences where individuals are measured according to some ordered variable, e.g. time. A common approach used to model such data is based on the mixed models for repeated measures. This model provides an eminently flexible approach to modeling of a wide range of mean and covariance structures. However, such models are forced into a rigidly defined class of mathematical formulas which may not be well supported by the data within the whole sequence of observations. A possible non-parametric alternative is a cubic smoothing spline, which is highly flexible and has useful smoothing properties. It can be shown that under normality assumption, the solution of the penalized log-likelihood equation is the cubic smoothing spline, and this solution can be further expressed as a solution of the linear mixed model. It is shown here how cubic smoothing splines can be easily used in the analysis of complete and balanced data. Analysis can be greatly simplified by using the unweighted estimator studied in the paper. It is shown that if the covariance structure of random errors belong to certain class of matrices, the unweighted estimator is the solution to the penalized log-likelihood function. This result is new in smoothing spline context and it is not only confined to growth curve settings. The connection to mixed models is used in developing a rough testing of group profiles. Numerical examples are presented to illustrate the techniques proposed.     

Reports of Conferences,Institutes, and Seminars

In the span of four weeks, from late March to early April 2008, three important technical services conferences were held: the Electronic Resources & Libraries Conference, the New England Technical Services Librarians Conference, and the North Carolina Serials Conference.     

Realistic Major Previews in the School‐to‐College Transition of Italian High School Students

Industrial/organizational researchers have reported that realistic job previews diminish prospective workers' expectations but promote the satisfaction and persistence of those who ultimately accept a job assignment. The authors applied this strategy to the context of school‐to‐college transition; 354 Italian high school students were provided with a 2‐hour lecture designed to simulate exposure to a college major of their choice. Students showed moderate pre‐post increases in subject matter knowledge but reported small decreases in interests and outcome expectations (but not self‐efficacy) related to the academic major to which they were exposed.     

Older Carers of Adults with a Learning Disability Confront the Future: Issues and Preferences in Planning

The Valuing People White Paper (Department of Health, 2001)requires services to secure a plan for all service-users withlearning disabilities living with older carers and promisesthem and their families more choice and control over how andwhere they live. This paper examines the views of the oldercarers (aged over seventy) of sixty-two adults with a learningdisability about planning for the future. Fifty-six took partin interviews in their own homes and six completed a questionnaire.All carers were white and recruited from one local authorityin response to the requirements of the White Paper. Findingsindicate that a significant proportion (thirty-four—55per cent) is either not ready or is unwilling to make futureplans. Barriers to planning include a perceived lack of needdue to the existence of two carers, a lack of awareness of timescalesinvolved in securing housing, difficulties in letting go, alack of confidence in available housing options, and the existenceof mutually supportive relationships. The findings show a needfor a proactive approach to information and support provisionto enable these families to work through a process of makingplans for the future. This is essential to prevent the needfor emergency placements in response to crisis and in turn toensure that adults with learning disabilities have genuine choiceand involvement in how and where they live.     

Exponential Family Models for sampled and census Network data

Much progress has been made on the development of statistical methods for network analysis in the past ten years, building on the general class of exponential family random graph (ERG) network models first introduced by Holland and Leinhardt (1981) . Recent examples include models for Markov graphs, "p*" models, and actor‐oriented models. For empirical application, these ERG models take a logistic form, and require the equivalent of a network census: data on all dyads within the network. In a largely separate stream of research, conditional log‐linear (CLL) models have been adapted for analyzing locally sampled ("egocentric") network data. While the general relation between log‐linear and logistic models is well known and has been exploited in the case of a priori blockmodels for networks, the relation for the CLL models is different due to the treatment of absent ties. For fully saturated tie independence models, CLL and ERG are equivalent and related via Bayes' rule. For other tie independence models, the two do not yield equivalent predicted values, but we show that in practice the differences are unlikely to be large. The alternate conditioning in the two models sheds light on the relationship between local and complete network data, and the role that models can play in bridging the gap between them.