Statistical evaluation of research performance of young university scholars: A case study

The research performance of a small group of 49 young scholars, such as doctoral students, postdoctoral and junior researchers, working in different technical and scientific fields, was evaluated based on 11 types of research outputs. The scholars worked at a technical university in the fields of Civil Engineering, Ecology, Economics, Informatics, Materials Engineering, Mechanical Engineering, and Safety Engineering. Principal Component Analysis was used to statistically analyze the research outputs and its results were compared with factor and cluster analysis. The metrics of research productivity describing the types of research outputs included the number of papers, books and chapters published in books, the number of patents, utility models and function samples, and the number of research projects conducted. The metrics of citation impact included the number of citations and h-index. From these metrics -the variables -the principal component analysis extracted 4 main principal components. The 1st principal component characterized the cited publications in high-impact journals indexed by the Web of Science. The 2nd principal component represented the outputs of applied research and the 3rd and 4th principal components represented other kinds of publications. The results of the principal component analysis were compared with the hierarchical clustering using Ward's method. The scatter plots of the principal component analysis and the Mahalanobis distances were calculated from the 4 main principal component scores, which allowed us to statistically evaluate the research performance of individual scholars. Using variance analysis, no influence of the field of research on the overall research performance was found. Unlike the statistical analysis of individual research metrics, the approach based on the principal component analysis can provide a complex view of the research systems.Web of Science30217716

A Methodology to Improve PCI Use in Industry

This article presents the development of a methodology using decision trees to resolve issues in industry with using process capability indices (PCIs). The methodology forms the structure of a prototype decision support system (PDSS) for PCI selection, calculation, and interpretation. Download instructions for the PDSS are available at http://program.20m.com

A Nonparametric Approach to Evaluating Inflation-Targeting Regimes

We use a variety of nonparametric test statistics to evaluate the inflation- targeting regimes of Australia, Canada, New Zealand, Sweden and the UK. We argue that a sensible approach of evaluation must rely on a variety of methods, among them parametric and nonparametric econometric methods, for robustness and completeness. Our evaluation strategy is based on examining two possible policy implications of inflation targeting: First, a welfare implication and second, a real variability implication. The welfare implication involves evaluating a utility function, and tested by testing whether (1) the distributions of the levels and the growth rates of private consumption and leisure per capita remained unchanged under inflation targeting, i.e., first-order stochastic dominance; and (2) testing a linear combination of consumption and leisure per capita, where the parameter describing the utility of leisure or the relative preference of leisure is calibrated. Then we introduce nonparametric univariate and multivariate statistical methods to test whether the first and second moments of a variety of real variables, such as the real exchange rate depreciation rate, real GDP per capita growth rate in addition to private consumption per capita and leisure per capita growth rates, remained unchanged under inflation targeting, decreased or increased significantly. There seems to be some evidence of increased welfare under inflation-targeting regimes, but no concrete evidence is found that inflation targeting policy, in general, reduces real variability. Some cross country differences are also found.Nonparametric, First-order stochastic dominance, sudden shift in the distribution, inflation targeting.

Diagnosis of a unit-wide disturbance caused by saturation in a manipulated variable

It is well known that faulty control valves with friction in the moving parts lead to limit cycle oscillations which can propagate to other parts of the plant. However, a control loop with healthy valve can also undergo oscillatory behavior. The root cause of a unit-wide oscillation in a distillation column was traced to a pressure control loop in a case study at Mitsui Chemicals. The diagnosis was made by means of a new technique of pattern matching of the time-resolved frequency spectrum using a wavelet analysis tool. The method identified key characteristics shared by measurements at various places in the column and quantified the similarities. Non-linearity was detected in the time trend of the pressure measurement, a result which initially suggested the root cause was a faulty actuator or sensor. Further analysis showed, however, that the source of non-linearlity was periodic saturation of the manipulated variable caused by slack tuning. The problem was remidied by changing the controller tuning settings and the unit-wide disturbance then went away

Modeling Data Containing Outliers using ARIMA Additive Outlier (ARIMA-AO)

The aim this study is discussed on the detection and correction of data containing the additive outlier (AO) on the model ARIMA (p, d, q). The process of detection and correction of data using an iterative procedure popularized by Box, Jenkins, and Reinsel (1994). By using this method we obtained an ARIMA models were fit to the data containing AO, this model is added to the original model of ARIMA coefficients obtained from the iteration process using regression methods. This shows that there is an improvement of forecasting error rate data.Comment: 13 page

On Phase II Monitoring of the Probability Distributions of Univariate Continuous Processes

Statistical process control (SPC) charts are widely used in industry for monitoring the stability of certain sequential processes like manufacturing, health care systems etc. Most SPC charts assume that the parametric form of the “in-control” process distribution F1 is available. However, it has been demonstrated in the literature that their performances are unreliable when the pre-specified process distribution is incorrect. Moreover, most SPC charts are designed to detect any shift in mean and/or variance. In real world problems, shifts in higher moments can happen without much change in mean or variance. If we fail to detect those and let the process run, it can eventually become worse and a shift in mean or variance can creep in. Moreover, the special cause that initiated the shift can inflict further damage to the system, and it may become a financial challenge to fix it. This paper provides an efficient and easy-to-use control chart for Phase II monitoring of univariate continuous processes when the parametric form of the “in-control” process distribution is unknown, but Phase I observations that are believed to be i.i.d. realizations from unknown F1 are available. Data-driven practical guidelines are also provided to choose the tuning parameter and the corresponding control limit of the proposed SPC chart. Numerical simulations and a real-life data analysis show that it can be used in many practical applications

Distribution-free cumulative sum control charts using bootstrap-based control limits

This paper deals with phase II, univariate, statistical process control when a set of in-control data is available, and when both the in-control and out-of-control distributions of the process are unknown. Existing process control techniques typically require substantial knowledge about the in-control and out-of-control distributions of the process, which is often difficult to obtain in practice. We propose (a) using a sequence of control limits for the cumulative sum (CUSUM) control charts, where the control limits are determined by the conditional distribution of the CUSUM statistic given the last time it was zero, and (b) estimating the control limits by bootstrap. Traditionally, the CUSUM control chart uses a single control limit, which is obtained under the assumption that the in-control and out-of-control distributions of the process are Normal. When the normality assumption is not valid, which is often true in applications, the actual in-control average run length, defined to be the expected time duration before the control chart signals a process change, is quite different from the nominal in-control average run length. This limitation is mostly eliminated in the proposed procedure, which is distribution-free and robust against different choices of the in-control and out-of-control distributions.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS197 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Monitoring Variability in Complex Manufacturing Process: Data Analysis Viewpoint with Application

To relate the control limits of Shewhart-type chart to the p-value, the control charting techniques were constructed based on statistical inference scheme. However, in daily practice of complex process variability (CPV) monitoring operation, these limits have nothing to do with the p-value. We cannot put any number to p. Instead, p is just read as “most probablyâ€. These words mean that in practice we are finally working under data analysis scheme instead. For this reason, in this paper we introduce the application of STATIS in CPV monitoring operation. It is a data analysis method to label the sample(s) where anomalous covariance structure occurs. This method is algebraic in nature and dominated by principal component analysis (PCA) principles. The relative position of a covariance matrix among others is visually presented along the first two eigenvalues of the so-called “scalar product matrix among covariance matricesâ€. Its strength will be illustrated by using a real industrial example and the results, compared with those given by the current methods, are very promising. Additionally, root causes analysis is also provided. However, since STATIS is a PCA-like, it does not provide any control chart, i.e., the history of process performance. It is to label the anomalous sample(s). To the knowledge of the authors, the application of STATIS in complex statistical process control is an unprecedented. Thus, it will enrich the literature of this field