Monitoring and change-point estimation for spline-modeled non-linear profiles in phase II

In some applications of statistical quality control, quality of a process or a product is best characterized by a functional relationship between a response variable and one or more explanatory variables. This relationship is referred to as a profile. In certain cases, the quality of a process or a product is better described by a non-linear profile which does not follow a specific parametric model. In these circumstances, nonparametric approaches with greater flexibility in modeling the complicated profiles are adopted. In this research, the spline smoothing method is used to model a complicated non-linear profile and the Hotelling T² control chart based on the spline coefficients is used to monitor the process. After receiving an out-of-control signal, a maximum likelihood estimator is employed for change point estimation. The simulation studies, which include both global and local shifts, provide appropriate evaluation of the performance of the proposed estimation and monitoring procedure. The results indicate that the proposed method detects large global shifts while it is very sensitive in detecting local shifts.     

A Comparison of the Performance of Phase II Simple Linear Profile Control Charts when Parameters are Estimated

The standard deviation of the average run length (SDARL) is an important performance metric in studying the performance of control charts with estimated in-control parameters. Only a few studies in the literature, however, have considered this measure when evaluating control chart performance. The current study aims at comparing the in-control performance of three phase II simple linear profile monitoring approaches; namely, those of Kang and Albin (2000), Kim et al. (2003), and Mahmoud et al. (2010). The comparison is performed under the assumption of estimated parameters using the SDARL metric. In general, the simulation results of the current study show that the method of Kim et al. (2003) has better overall statistical performance than the competing methods in terms of SDARL values. Some of the recommended approaches based solely on the usual average run length properties can have poor SDARL performance.     

天津市高职示范校教学质量监控体系建设现状探析

基于天津市高职示范院校,从教学质量监控中的评价主体、操作方法、评价范围、评价结果反馈等多个方面进行阐述;查找当前存在的问题并提出改进建议。     

The Development of a Screening Tool for the Adult Children of Alcoholics Traits (STACAT)

In order to develop a valid tool for screening the traits of adult children of alcoholics (ACOA), this study focused on finding common characteristics among ACOA and developing a tool based on those characteristics. An item pool was generated targeting three distinct characteristics discussed in the alcohol literature related to ACOA, and the items were reviewed by a panel of experts. Using data from the sample, a confirmatory factor analysis, t -test and correlation analysis were conducted to gather various validity evidences for the screening tool. Cronbach's coefficient alpha test was used to check the internal consistency of the tool. The evidence of the reliability and validity for the ACOA traits screening tool provide the social work practice with an empirically validated method of measuring these traits in the children of alcoholics.     

家长期望教育价值的思考与探索

家长对子女的学习成绩、学历水平、职业选择等关系他们一生成长的重要方面均有很高的期望。要克服长期存在的家长期望过高的偏向以及由此给儿童、青少年身心发展和学校教育带来的负面影响,必须正确认识家长期望的教育意义,给家长期望适切、合理的教育定位,准确把握家长期望的水平尺度,不断提高家长期望质量。这样,才能充分发挥家长期望特有的教育功能,实现家长期望的教育价值。     

Structural Change Monitoring for Random Coefficient Autoregressive Time Series

A monitoring scheme is proposed to sequentially detect a structural change in random coefficient autoregressive time series of order p (RCA(p)) after a training period of size T. It extends structural change monitoring to RCA(p) time series. The asymptotic properties of our monitoring statistic are established under both the null of no change in parameters and the alternative of a change in coefficient. The finite sample properties are investigated by a simulation study.     

Modelling of connected processes

The problem of comparing, contrasting and combining information from different sets of data is an enduring one in many practical applications of statistics. A specific problem of combining information from different sources arose in integrating information from three different sets of data generated by three different sampling campaigns at the input stage as well as at the output stage of a grey-water treatment process. For each stage, a common process trend function needs to be estimated to describe the input and output material process behaviours. Once the common input and output process models are established, it is required to estimate the efficiency of the grey-water treatment method. A synthesized tool for modelling different sets of process data is created by assembling and organizing a number of existing techniques: (i) a mixed model of fixed and random effects, extended to allow for a nonlinear fixed effect, (ii) variogram modelling, a geostatistical technique, (iii) a weighted least squares regression embedded in an iterative maximum-likelihood technique to handle linear/nonlinear fixed and random effects and (iv) a formulation of a transfer-function model for the input and output processes together with a corresponding nonlinear maximum-likelihood method for estimation of a transfer function. The synthesized tool is demonstrated, in a new case study, to contrast and combine information from connected process models and to determine the change in one quality characteristic, namely pH, of the input and output materials of a grey-water filtering process.     

Bayesian monitoring of local residual autocorrelations taking into account the run-length

This paper proposes new two-sided monitoring algorithms for detecting the presence of first order residual autocorrelations in Dynamic Normal Models. The methodology uses a Bayesian decision approach with loss function which takes into account the run-length of the process. The power and mean run-length of the proposed algorithms are analysed by Monte Carlo methods. The results obtained improve those corresponding to the monitoring algorithm for residual autocorrelations proposed in Gargallo and Salvador [2003. Monitoring residual autocorrelations in dynamic linear models. Comm. Statist. Simulation Comput. 32(4), 1079–1104.] with respect to the run-length, and also exhibit more homogeneous behaviour.     

Longitudinal links of parental solicitation,knowledge, and peer approval with deviance during early adolescence

The current study examined growth-to-growth associations of parental solicitation, knowledge, and peer approval with deviance during early adolescence, using a 4-wave, 18-month self-reported longitudinal data set from 570 Czech early adolescents (58.4% female; M_age = 12.43 years, SD = 0.66 at baseline). Unconditional growth model tests provided evidence of significant changes in the three parenting behaviors and in deviance over time. Multivariate growth model tests showed that declines in maternal knowledge were associated with increases in deviance, while greater increases in parental peer approval were associated with slower increases in deviance. Findings provide evidence of dynamic changes in parental solicitation, knowledge, and peer approval over time, as well as in deviance; additionally, they importantly show how parental knowledge and peer approval covary developmentally with deviance.