Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality - process characteristics is necessary. Process monitoring problems in which several related variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation

Long-run marketing inferences from scanner data.

Good marketing decisions require managers' understanding of the nature of the market-response function relating performance measures such as sales and market share to variations in the marketing mix (product, price, distribution and communications efforts). Our paper focuses on the dynamic aspect of market-response functions, i.e. how current marketing actions affect current and future market response. While conventional econometrics has been the dominant methodology in empirical market-response analyses, time-series analysis offers unique opportunities for pushing the frontier in dynamic research. This paper examines the contributions an d the future outlook of time-series analysis in market-response modeling. We conclude first, that time series analysis has made a relatively limited overall contribution to the discipline, and investigate reasons why that has been the case. However, major advances in data (transactions-based databases and in modeling technology (long-term time-series modeling) create new opportunities for time-series techniques in marketing, in particular for the study of long-run marketing effectiveness. We discuss four major aspects of long -term time-series modeling, relate them to substantive marketing problems, and describe some early applications. Combining the new data with the new methods, we then present original empirical results on the long-term behavior of brand sales and category sales for four consumer products. We discuss the implications of our findings for future research in market response. Our observations lead us to identify three areas where additional research could enhance the diffusion of the identified time-series concepts in marketing.Data; Marketing;

Multivariate Statistical Process Control Charts: An Overview

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Multivariate control charts based on Bayesian state space models

This paper develops a new multivariate control charting method for vector autocorrelated and serially correlated processes. The main idea is to propose a Bayesian multivariate local level model, which is a generalization of the Shewhart-Deming model for autocorrelated processes, in order to provide the predictive error distribution of the process and then to apply a univariate modified EWMA control chart to the logarithm of the Bayes' factors of the predictive error density versus the target error density. The resulting chart is proposed as capable to deal with both the non-normality and the autocorrelation structure of the log Bayes' factors. The new control charting scheme is general in application and it has the advantage to control simultaneously not only the process mean vector and the dispersion covariance matrix, but also the entire target distribution of the process. Two examples of London metal exchange data and of production time series data illustrate the capabilities of the new control chart.Comment: 19 pages, 6 figure

Test for Breaks in the Conditional Co-Movements of Asset Returns

We propose procedures designed to uncover structural breaks in the co-movements of financial markets. A reduced form approach is introduced that can be considered as a two stage method for reducing dimensionality of multivariate heteroskedastic conditional volatility models through marginalization. The main advantage is that one can use returns normalized by volatility filters that are purely data-driven and construct general conditional covariance dynamic specifications. The main thrust of our procedure is to examine change-points in the co-movements of normalized returns. We document, using a ten year period of two representative high frequency FX series, that regression models with non-Gaussian errors describe adequately their co-movements. Change-points are detected in the conditional covariance of the DM/US$and YN/US$ normalized returns over the decade 1986-1996.change-point tests, conditional covariance, high-frequency financial data, multivariate GARCH models

25 Years of IIF Time Series Forecasting: A Selective Review

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982-1985; International Journal of Forecasting 1985-2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series forecasting that have been published elsewhere during this period. Enormous progress has been made in many areas, but we find that there are a large number of topics in need of further development. We conclude with comments on possible future research directions in this field.Accuracy measures; ARCH model; ARIMA model; Combining; Count data; Densities; Exponential smoothing; Kalman Filter; Long memory; Multivariate; Neural nets; Nonlinearity; Prediction intervals; Regime switching models; Robustness; Seasonality; State space; Structural models; Transfer function; Univariate; VAR.

Forecasting the Volatility of Australian Stock Returns: Do Common Factors Help?

This paper develops univariate and multivariate forecasting models for realized volatility in Australian stocks. We consider multivariate models with common features or common factors, and we suggest estimation procedures for approximate factor models that are robust to jumps when the cross-sectional dimension is not very large. Our forecast analysis shows that multivariate models outperform univariate models, but that there is little difference between simple and sophisticated factor models.

Microdata Disclosure by Resampling: Empirical Findings for Business Survey Data

A problem statistical offices and research institutes are faced with by releasing micro-data is the preservation of confidentiality. Traditional methods to avoid disclosure often destroy the structure of data, i.e., information loss is potentially high. In this paper I discuss an alternative technique of creating scientific-use-files, which reproduce the characteristics of the original data quite well. It is based on Fienberg?s (1997 and 1994) [5], [6] idea to estimate and resample from the empirical multivariate cumulative distribution function of the data in order to get synthetic data. The procedure creates datasets - the resample - which have the same characteristics as the original survey data. In this paper I present some applications of this method with (a) simulated data and (b) innovation survey data, the Mannheim Innovation Panel (MIP), and compare resampling with a common method of disclosure control, i.e. disturbance with multiplicative error, concerning confidentiality on the one hand and the appropriateness of the disturbed data for different kinds of analyses on the other. The results show that univariate distributions can be better reproduced by unweighted resampling. Parameter estimates can be reproduced quite well if (a) the resampling procedure implements the correlation structure of the original data as a scale and (b) the data is multiplicative perturbed and a correction term is used. On average, anonymized data with multiplicative perturbed values better protect against re?identification as the various resampling methods used. --resampling,multiplicative data perturbation,Monte Carlo studies,business survey data