Profile monitoring is an approach in quality control best used where the process data follow a profile (or curve). The majority of previous studies in profile monitoring focused on the parametric (P) modeling of either linear or nonlinear profiles, with both fixed and random effects, under the assumption of correct model specification. More recently, in the absence of an obvious P model, nonparametric (NP) methods have been employed in the profile monitoring context. For situations where a P model is adequate over part of the data but inadequate of other parts, we propose a semiparametric procedure that combines both P and NP profile fits. We refer to our semiparametric procedure as mixed model robust profile monitoring (MMRPM). These three methods (P, NP and MMRPM) can account for the autocorrelation within profiles and treat the collection of profiles as a random sample from a common population. For each approach, we propose a version of Hotelling's T 2 statistic for use in Phase I analysis to determine unusual profiles based on the estimated random effects and obtain the corresponding control limits. Simulation results show that our MMRPM method performs well in making decisions regarding outlying profiles when compared to methods based on a misspecified P model or based on NP regression. In addition, however, the MMRPM method is robust to model misspecification because it also performs well when compared to a correctly specified P model. The proposed chart is able to detect changes in Phase I data and has easily calculated control limits. We apply all three methods to the automobile engine data of Amiri et al. 5 and find that the NP and the MMRPM methods indicate signals that did not occur in a P approach.

The adaptive exponentially weighted moving average (AEWMA) control chart has the advantage of detecting balance mixed range of mean shifts. Its performance has been studied under the assumption that the process parameters are known. Under this assumption, previous studies have shown AEWMA to provide superior statistical performance when compared with other different types of control charts. In practice, however, the process parameters are usually unknown and are required to be estimated. Using a Markov Chain approach, we show that the performance of the AEWMA control chart is affected when parameters are estimated compared with the known-parameter case. In addition, we show the effect of different standard deviation estimators on the chart performance. Finally, a performance comparison is conducted between the exponentially weighted moving average (EWMA) chart and the AEWMA chart when the process parameters are unknown. We recommend the use of the AEWMA chart over the ordinary EWMA chart especially when a small number of Phase I samples is available to estimate the unknown parameters.

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We review some prospective scan-based methods that are used in health-related applications to detect increased rates of mortality or morbidity and to detect bioterrorism or active clusters of disease. We relate these methods to the use of the moving average chart in industrial applications. Issues that are related to the performance evaluation of spatiotemporal scan-based methods are discussed. In particular we clarify the definition of a recurrence interval and demonstrate that this measure does not reflect some important aspects of the statistical performance of scan-based, and other, surveillance methods. Some research needs in this area are given. Copyright 2008 Royal Statistical Society.

Abstract. β1,4-Galactosylransferases are a family of enzymes encoded by seven B4GALT genes and are involved in the development of anticancer drug resistance and metastasis. Among these genes, the B4GALT1 shows significant variations in the transcript origination sites in different cell types/tissues and encodes an interesting dually partitioning β-1, 4-galactosyltransferase protein. We identified at 5'-end of B4GALT1 a 1.454 kb sequence forming a transcription regulatory region, referred to by us as the TR1-PE1, had all characteristics of a bidirectional promoter directing the transcription of B4GALT1 in a divergent manner along with its long non-coding RNA (lncRNA) antisense counterpart B4GALT1-AS1. The TR1-PE1 showed unique dinucleotide base-stacking energy values specific to transcription factor binding sites (TFBSs), INR and BRE, and harbored CpG Island (CGI) that showed GC skew with potential for R-loop formation at the transcription starting sites (TSSs). The 5'-regulatory axis of B4GALT1 also included five more novel TFBSs for CTCF, GLI1, TCF7L2, GATA3 and SOX5, in addition to unique (TG) 18 repeats in conjunction with 22 nucleotide TG-associated sequence (TGAS). The five lncRNA B4GALT1-AS1 transcripts showed significant complementarity with B4GALT1 mRNA. In contrast, the rest of B4GALT genes showed fewer lncRNAs, and all lacked the (TG) 18 and TGAS. Our results are strongly supported by the FANTOM5 study which showed tissue-specific variations in transcript origination sites for this gene. We suggest that the unique expression patterns for the B4GALT1 in normal and malignant tissues are controlled by a differential usage of 5'-B4GALT1 regulatory units along with a post-transcriptional regulation by the antisense RNA, which in turn govern the cell-matrix interactions, neoplastic progression, anticancer drug sensitivity, and could be utilized in personalized therapy.