In the early 1960s, Shiryaev obtained the structure of Bayesian stopping rules for detecting abrupt changes in independent and identically distributed sequences as well as in a constant drift of the Brownian motion. Since then, the methodology of optimal change-point detection has concentrated on the search for stopping rules that achieve the best balance of the mean detection delay and the rate of false alarms or minimize the mean delay under a fixed false alarm probability. In this respect, analysis of the performance of the Shiryaev procedure has been an open problem. Recently, Tartakovsky and Veeravalli (2005) investigated asymptotic performance of the Shiryaev Bayesian change detection procedure, the Page procedure, and the Shiryaev-Roberts procedure when the false alarm probability goes to zero for general discrete-time models. In this article, we investigate the asymptotic performance of Shiryaev and Shiryaev-Roberts procedures for general continuous-time stochastic models for a small false alarm probability and small cost of detection delay. We show that the Shiryaev procedure has asymptotic optimality properties under mild conditions, while the Shiryaev-Roberts procedure may or may not be asymptotically optimal depending on the type of the prior distribution. The presented asymptotic Bayesian detection theory substantially generalizes previous work in the field of change-point detection for continuous-time processes.

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Sequential procedures are developed for simultaneous testing of multiple hypotheses in sequential experiments. Proposed stopping rules and decision rules achieve strong control of both family-wise error rates I and II. The optimal procedure is sought that minimizes the expected sample size under these constraints. Bonferroni methods for multiple comparisons are extended to sequential setting and are shown to attain an approximately 50% reduction in the expected sample size compared with the earlier approaches. Asymptotically optimal procedures are derived under Pitman alternative.

Drug use has been associated with craving, which may be described as a powerful and sometimes overwhelming urge to use the drug. Patients seeking treatment for methylamphetamine dependence must cope with drug cravings as they engage in psychosocial treatments. Changes in brain GABA(A) receptors during substance use and withdrawal provide a neurobiological basis for craving and associated anxiety. Flumazenil (a benzodiazepine antagonist) plus gabapentin (an antiepileptic) were compared with placebo in a randomized, double-blind study to assess the effects on craving during initial treatment for methylamphetamine dependence. Evaluation was conducted over a 30-day period. Craving and drug use were found to be highly correlated. Craving was reduced significantly in the flumazenil plus gabapentin group compared with placebo following the initial treatment period and throughout the 30 days. Decreased methylamphetamine use was also observed, as measured by urine drug screens and self-reports.

Gulf War Illness (GWI) is a multisymptom disorder including widespread chronic pain, fatigue and gastrointestinal problems. The objective of this study was to examine the low glutamate diet as a treatment for GWI. Forty veterans with GWI were recruited from across the US. Outcomes included symptom score, myalgic score, tender point count, dolorimetry and the Chalder Fatigue Scale. Subjects were randomized to the low glutamate diet or a wait-listed control group, with symptom score being compared after one month. Subjects then went onto a double-blind, placebo-controlled crossover challenge with monosodium glutamate (MSG)/placebo to test for return of symptoms. Symptom score was compared between diet intervention and wait-listed controls with an independent t-test and effect size was calculated with Cohen’s d. Change scores were analyzed with Wilcoxon Signed Rank tests. Crossover challenge results were analyzed with General Linear Models and cluster analysis. The diet intervention group reported significantly less symptoms (p = 0.0009) than wait-listed controls, with a very large effect size, d = 1.16. Significant improvements in average dolorimetry (p = 0.0006), symptom score, tender point number, myalgic score and the Chalder Fatigue Scale (all p < 0.0001) were observed after the 1-month diet. Challenge with MSG/placebo resulted in significant variability in individual response. These results suggest that the low glutamate diet can effectively reduce overall symptoms, pain and fatigue in GWI, but differential results upon challenge suggest that other aspects of the diet, or underlying differences within the population, may be driving these changes. Future research is needed to identify potential nutrient effects, biomarkers, and underlying metabolic differences between responders and non-responders.

Rich;tr(lsu~i. Texas 75083-0658 K P~ words and Phrases: CUSUM; gener-ulued likelthood r,atzo. 1rircrr1 delny: m e a n tzme between false alarms; stopping t i~r~~. ABSTRACT Under standard conditions of change-point problems with one or both distributions being unknown, we propose efficient on-line and off-line n o n p a r a~r~r t r i c algorithms for detecting and estimating the change-point. They are based 011 histogram density estimators, which allows applications involving ordinal and categorical data. Also, they are designed to detect any changes in distribution, not necessarily related to the location or scale parameters. EfFicienry of the proposed schemes is demonstrated by relevant inequalities for the rnean delay Downloaded by [Otto-von-Guericke-Universitaet Magdeburg] at 03:36 14 October 2014 2 BARON and the mean time between false alarms. Asymptotically, they are shown to behave similarly t o the most efficient procedures based on the known distributions The stopping rule achieves an asymptotically linear mean delay and an exponential mean time between false alarms. The guidelin~s on selecting the threshold and the partition for the histogram density estimation are given, based on the obtained results. Proposed methods are applied to the England temperatures data and the Vostok ice core record to detect the glol~al climate changes

Most approaches to classifying data streams either divide the stream into fixed-size chunks or use gradual forgetting. Due to evolving nature of data streams, finding a proper size or choosing a forgetting rate without prior knowledge about time-scale of change is not a trivial task. These approaches hence suffer from a trade-off between performance and sensitivity. Existing dynamic sliding window based approaches address this problem by tracking changes in classifier error rate, but are supervised in nature. We propose an efficient semi-supervised framework in this paper which uses change detection on classifier confidence to detect concept drifts, and to determine chunk boundaries dynamically. It also addresses concept evolution problem by detecting outliers having strong cohesion among themselves. Experiment results on benchmark and synthetic data sets show effectiveness of the proposed approach.