Looking Outside the (Voice)Box

Laura S. Brown, PhD, is a clinical and forensic psychologist in independent practice in Seattle, Washington. The bulk of her scholarly work has been in the fields of feminist therapy theory, trauma treatment, lesbian and gay issues, assessment and diagnosis, ethics and standards of care in psychotherapy, and cultural competence. She has authored or edited ten professional books, including the award-winning Subversive Dialogues: Theory in Feminist Therapy, as well as more than 140 other professional publications. She has also recently published her first book for general audiences, Your turn for care: Surviving the aging and death of the adults who harmed you. Laura has been featured in five psychotherapy training videos produced by the American Psychological Association. She was President of American Psychological Association Divisions 35 (Society for the Psychology of Women), 44 (Society for the Psychological Study of Lesbian, Gay and Bisexual Issues), and 56 (Trauma Psychology). Laura was also President of the Washington State Psychological Association. She is the founder and Director of the Fremont Community Therapy Project, a low-fee psychotherapy training clinic in Seattle. In the fall of 2000, she was the on-site psychologist for the reality show Survivor: The Australian Outback. In 1987, Laura lost her voice and was diagnosed with spasmodic dysphonia. In 1988, she found her voice again.     

Multivariate Bayesian variable selection and prediction

The multivariate regression model is considered with p regressors. A latent vector with p binary entries serves to identify one of two types of regression coefficients: those close to 0 and those not. Specializing our general distributional setting to the linear model with Gaussian errors and using natural conjugate prior distributions, we derive the marginal posterior distribution of the binary latent vector. Fast algorithms aid its direct computation, and in high dimensions these are supplemented by a Markov chain Monte Carlo approach to sampling from the known posterior distribution. Problems with hundreds of regressor variables become quite feasible. We give a simple method of assigning the hyperparameters of the prior distribution. The posterior predictive distribution is derived and the approach illustrated on compositional analysis of data involving three sugars with 160 near infrared absorbances as regressors.     

Summary measurements and screening in clinical trials with replicate observations

Repeating measurements of efficacy variables in clinical trials may be desirable when the measurement may be affected by ambient conditions. When such measurements are repeated at baseline and at the end of therapy, statistical questions relate to: (1) the best summary measurement to use for a subject when there is a possibility that some observations are contaminated and have increased variances; and (2) the effect of screening procedures which exclude outliers based on within- and between-subject contamination tests. We study these issues in two stages, each using a different set of models. The first stage deals only with the choice of the summary measure. The simulation results show that in some cases of contamination, the power achieved by the tests based on the median exceeds that achieved by the tests based on the mean of the replicates. However, even when we use the median, there are cases when contamination leads to a considerable loss in power. The combined issue of the best summary measurement and the effect of screening is studied in the second stage. The tests use either the observed data or the data after screening for outliers. The simulation results demonstrate that the power depends on the screening procedure as well as on the test statistic used in the study. We found that for the extent and magnitude of contamination considered, within-subject screening has a minimal effect on the power of the tests when there are at least three replicates; as a result, we found no advantage in the use of screening procedures for within-subject contamination. On the other hand, the use of a between-subject screening for outliers increases the power of the test procedures. However, even with the use of screening procedures, heterogeneity of variances can greatly reduce the power of the study.     

Nonparametric smoothing using state space techniques

The authors examine the equivalence between penalized least squares and state space smoothing using random vectors with infinite variance. They show that despite infinite variance, many time series techniques for estimation, significance testing, and diagnostics can be used. The Kalman filter can be used to fit penalized least squares models, computing the smoothed quantities and related values. Infinite variance is equivalent to differencing to stationarity, and to adding explanatory variables. The authors examine constructs called “smoothations” which they show to be fundamental in smoothing. Applications illustrate concepts and methods.     

NONPARAMETRIC ESTIMATION OF MULTIVARIATE CONVEX-TRANSFORMED DENSITIES

We study estimation of multivariate densities p of the form p(x) = h(g(x)) for x ∈ ?(d) and for a fixed monotone function h and an unknown convex function g. The canonical example is h(y) = e(-y) for y ∈ ?; in this case, the resulting class of densities [Formula: see text]is well known as the class of log-concave densities. Other functions h allow for classes of densities with heavier tails than the log-concave class.We first investigate when the maximum likelihood estimator p? exists for the class P(h) for various choices of monotone transformations h, including decreasing and increasing functions h. The resulting models for increasing transformations h extend the classes of log-convex densities studied previously in the econometrics literature, corresponding to h(y) = exp(y).We then establish consistency of the maximum likelihood estimator for fairly general functions h, including the log-concave class P(e(-y)) and many others. In a final section, we provide asymptotic minimax lower bounds for the estimation of p and its vector of derivatives at a fixed point x(0) under natural smoothness hypotheses on h and g. The proofs rely heavily on results from convex analysis.     

An Interview with Ben Brown,Co-founder of So New Media and Publisher of Words! Words! Words!

Abstract

With this installment of “They Might Be Giants,” Michael Brown talks with Ben Brown (no relation) about publishing books (ten so far!) and magazines (issue 3 of Words! Words! Words! is about to be released), while at the same time working nine-to-five at a really boring tech job. It's a do-it-yourselfer's dream come true. Serials Review 2002; 28:344–348.     

Local influence regression diagnostics for gamma data

The local influence diagnostics proposed in general terms irl Cook (1986), Thomas and Cook (1989) and Billor and Loynes (1993). arc adapted for gamma data. A data set prcviously analysed using different methods is then reexamined, and conclusions based on wing the new approach are made.     

Gap-based inverse sampling

We present a new inverse sampling design for surveys of rare events, Gap-Based Inverse Sampling. In the design, sampling stops if after a predetermined interval, or gap, no new rare events are found. The length of the gap that follows after finding a rare event is used as a way of limiting sample effort. We present stopping rules using decisions based on the gap length, the total number of rare events found, and a fixed upper limit of survey effort. We illustrate the use of the design with stratified sampling of two biological populations. The design uses the intuitive behavior of a field biologist in stratified sampling, where if in a stratum nothing is found after a long search, the field surveyor would like to consider the stratum is empty and stop searching. Our design has appeal for surveying rare events (for example, a rare species) with stratified sampling where there are likely to be some completely empty strata.     

Comparing Career Development Outcomes Among Undergraduate Students in Cognitive Information Processing Theory–Based Versus Human Relations Courses

The effectiveness of undergraduate career courses has been demonstrated for multiple variables, including career certainty, maturity, decision-making skills, and reduction of dysfunctional career thoughts. Although such studies used the career course as an intervention, most failed to include a comparison course, nor were grounded in career theory. This study used a comparison group of 152 undergraduates enrolled in career development courses and 50 students enrolled in undergraduate human relations courses. Pre- and posttest comparisons indicated that the career course yielded significant improvements in career decision state, cognitive information processing (CIP) skills, career decision-making stage, knowledge of next steps, and anxiety about current career concern, but the human relations course did not. The CIP-based career course is supported as a valid career intervention, and individuals may benefit from targeted interventions depending on their CASVE cycle position. Future research might compare different career theory–based or atheoretical career courses on career development outcomes.