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.

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.

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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.

Profile monitoring is one of the methods used in statistical process control (SPC) to understand the functional relationship between response and explanatory variables by tracking this relationship and estimating parameters. SPC is done in two phases: In Phase I, a statistical model is created and its parameters estimated using historical data. Phase II implements the statistical model and monitors the live ongoing process. Control charts are graphical tools used to monitor these functional relationships over time in both Phase I and Phase II. This study provides a step‐by‐step application for parametric, nonparametric, and semiparametric methods in profile monitoring and creates an in‐depth guideline with comparative analysis studies for novice practitioners. A comparative analysis under each distributional assumption is conducted for various control charts.

In standard analyses of data well-modeled by a nonlinear mixed model, an aberrant observation, either within a cluster, or an entire cluster itself, can greatly distort parameter estimates and subsequent standard errors. Consequently, inferences about the parameters are misleading. This paper proposes an outlier robust method based on linearization to estimate fixed effects parameters and variance components in the nonlinear mixed model. An example is given using the four-parameter logistic model and bioassay data, comparing the robust parameter estimates with the nonrobust estimates given by SAS(®).

Aggregation of large databases in a specific format is a frequently used process to make the data easily manageable. Interval-valued data is one of the data types that is generated by such an aggregation process. Using traditional methods to analyze interval-valued data results in loss of information, and thus, several interval-valued data models have been proposed to gather reliable information from such data types. On the other hand, recent technological developments have led to high dimensional and complex data in many application areas, which may not be analyzed by traditional techniques. Functional data analysis is one of the most commonly used techniques to analyze such complex datasets. While the functional extensions of much traditional statistical techniques are available, the functional form of the interval-valued data has not been studied well. This paper introduces the functional forms of some well-known regression models that take interval-valued data. The proposed methods are based on the function-on-function regression model, where both the response and predictor/s are functional. Through several Monte Carlo simulations and empirical data analysis, the finite sample performance of the proposed methods is evaluated and compared with the state-of-the-art.Due to recent technological advances, the process of collecting data has become complicated, causing high dimensional and complex data structures. Symbolic data analysis is one of the commonly used methods in modeling such complex and large datasets, see Billard (2011) and Noirhomme-Fraiture and Brito (2011) for recent developments in symbolic data analysis. Contrary to single-valued observations in p-dimensional space where classical statistical methods work on, symbolic data may be in the form of hypercubes in p-dimensional space. There are many symbolic data types, for example, list, histogram, modal-valued, and interval-valued data. In this research, we restrict our attention to the interval-valued data only. The data expressed in an interval format (minimum and maximum values of the data) is called the interval-valued data. Such datasets are frequently encountered in daily life, for example, air and/or surface temperature, wind speed, energy production, blood pressure, and exchange rates. The main problem encountered during the modeling of the interval-valued data with classical statistical techniques is "how the variability of observations within the range is involved in modeling?". Traditional methods analyze interval-valued data using its summary (i.e., mid-points), and this approach results in loss of information. Therefore, interval-valued data analysis techniques are needed to obtain more accurate information.The early studies about the interval-valued data regression were conducted by Billard and Diday (2000), who extended traditional statistical techniques to the interval-valued data. Billard and Diday (2002) extended several classical regression models to interval-valued data and they proposed a regression equation for fitting histogram...