Guido Masarotto

An Adaptive Exponentially Weighted Moving Average Control Chart

Lucas and Saccucci showed that exponentially weighted moving average (EWMA) control charts can be designed to quickly detect either small or large shifts in the mean of a sequence of independent observations. But a single EWMA chart cannot perform well for small and large shifts simultaneously. Furthermore, in the worst-case situation, this scheme requires a few observations to overcome its initial inertia. The main goal of this article is to suggest an adaptive EWMA (AEWMA) chart that weights the past observations of the monitored process using a suitable function of the current “error.” The resulting scheme can be viewed as a smooth combination of a Shewhart chart and an EWMA chart. A design procedure for the new control schemes is suggested. Comparisons of the standard and worst-case average run length profiles of the new scheme with those of different control charts show that AEWMA schemes offer a more balanced protection against shifts of different sizes.

Distant metastases in differentiated thyroid cancer: Long-term results of radioiodine treatment and statistical analysis of prognostic factors in 214 patients

Long-term results and statistical analysis of prognostic factors in a series of 214 patients with distant metastases from differentiated thyroid cancer (DTC) are reported here. These 214 were part of a total series of 1457 patients with DTC referred to our center from 1967 to 1987. All patients underwent surgery and 131-I therapy and were treated with TSH suppressive doses of thyroid hormones. After a mean follow-up of 7.3 years including clinical, scintigraphic, radiological and laboratory investigations, 24.4 % of patients were alive without disease, 36.5 % alive with disease, 1.8 % dead without disease and 37.3 % dead with disease. One of the main factors influencing the survival in our series was 131-I uptake (RIU) by metastatic tissue. No case of complete remission of disease was observed among patients with nonfunctioning metastases. Another important factor was the site of metastases, patients with bone metastases having the worst prognosis. The patients age at diagnosis represented another important factor for survival; patients over 40 years, particularly those over 60 years had a bad prognosis. A clear interrelation was found among the factors advanced age, nonfunctioning metastases and bone metastases. Patients with these last clinical features were considered to be at high risk and generally had a fatal outcome. Another significant prognostic factor revealed by univariate analysis was the histologic type. Patients with follicular tumor showed a poorer prognosis in comparison to papillary tumor. When multivariate analysis was applied, the factors age at diagnosis, site of metastases and RIU proved to have a significant influence on survival, but not the histologic type. Lastly, the relative rate of males was higher in the group of patients with metastases in comparison to the whole series of DTC patients. Despite this, the factor sex did not influence survival.

Bootstrap prediction intervals for autoregressions

Abstract The bootstrap technique is applied to obtain interval forecasts for an autoregressive time series. The relevant features of the proposed method are: (i) it is distribution-free, and (ii) it explicitly takes into account that order and parameters of the model are estimated from the data.

Gaussian Copula Marginal Regression

This paper identifies and develops the class of Gaussian copula models for marginal regression analysis of non-normal dependent observa- tions. The class provides a natural extension of traditional linear regression models with normal correlated errors. Any kind of continuous, discrete and categorical responses is allowed. Dependence is conveniently modelled in terms of multivariate normal errors. Inference is performed through a like- lihood approach. While the likelihood function is available in closed-form for continuous responses, in the non-continuous setting numerical approx- imations are used. Residual analysis and a specification test are suggested for validating the adequacy of the assumed multivariate model. Methodol- ogy is implemented in a R package called gcmr. Illustrations include simu- lations and real data applications regarding time series, cross-design data, longitudinal studies, survival analysis and spatial regression.

Histological evaluation of thyroid carcinomas: reproducibility of the "WHO" classification.

Aims and Backgrounds Thyroid carcinomas display several pathologic features, show different behavior and necessitate different treatment; thus correct classification is mandatory. Methods The kappa statistic was used as a measure of agreement in a panel of seven pathologists who reviewed 200 cases of thyroid tumors. Results Overall agreement was 83 % (k = 68). Good agreement was found for anaplastic (k = 0.85) and papillary carcinomas (k = 0.81); agreement for medullary carcinoma was acceptable (k = 0.80), suboptimal for other (k = 0.67), and poor for follicular carcinoma (k = 0.54). Conclusions Central pathology review of thyroid carcinomas is recommended when clinical and epidemiologic trials are planned.

A Least Angle Regression Control Chart for Multidimensional Data

In multidimensional applications, it is very rare that all variables shift at the same time. A statistical process control procedure would have superior efficiency when limited to the subset of variables likely responsible for the out-of-control conditions. The key idea of this article consists of combining a variable selection method with a multivariate control chart to detect changes in both the mean and variability of a multidimensional process with Gaussian errors. In particular, we develop a control chart for Phase II monitoring which integrates the least angle regression algorithm with a multivariate exponentially weighted moving average. Comparisons with related multivariate control schemes demonstrate the efficiency of the proposed control chart in a wide range of practical applications, including profile and multistage process monitoring. Further, the proposed scheme may also provide valuable diagnostic information for fault isolation. Supplemental materials, including an R package, are available online.

Combined Shewhart–EWMA control charts with estimated parameters

Shewhart and EWMA control charts can be suitably combined to obtain a simple monitoring scheme sensitive to both large and small shifts in the process mean. So far, the performance of the combined Shewhart–EWMA (CSEWMA) has been investigated under the assumption that the process parameters are known. However, parameters are often estimated from reference Phase I samples. Since chart performances may be even largely affected by estimation errors, we study the behaviour of the CSEWMA with estimated parameters in both in- and out-of-control situations. Comparisons with standard Shewhart and EWMA charts are presented. Recommendations are given for Phase I sample size requirements necessary to achieve desired in-control performance.

Practical Design of Generalized Likelihood Ratio Control Charts for Autocorrelated Data

Control charts based on generalized likelihood ratio (GLR) tests are attractive from both theoretical and practical points of view. In particular, in the case of an autocorrelated process, the GLR test uses the information contained in the time-varying response after a change and, as shown by Apley and Shi, is able to outperfom traditional control charts applied to residuals. In addition, a GLR chart provides estimates of the magnitude and the time of occurrence of the change. In this article we present a practical approach to implementating GLR charts for monitoring an autoregressive moving average process assuming that only a phase I sample is available. The proposed approach, based on automatic time series identification, estimates the GLR control limits through stochastic approximation using bootstrap resampling and thus is able to take into account the uncertainty about the underlying model. A Monte Carlo study shows that our methodology can be used to design, in a semiautomatic fashion, a GLR chart with a prescribed rate of false alarms when as few as 50 phase I observations are available. A real example is used to illustrate the designing procedure.

Self-Starting CUSCORE Control Charts for Individual Multivariate Observations

In some manufacturing settings, such as during process start-up and in the case of short production runs, process parameters are unknown, and Phase I samples cannot be gathered to accurately estimate control limits for prospective monitoring. Self-starting charts can be applied to these low-volume applications. In this article, two new self-starting multivariate control charts, both based on a CUSCORE-type procedure, are proposed for monitoring the unknown mean of a multivariate normal distribution. These charting procedures, which weight current observations according to the information contained in the fault signature, are able to outperform the previously suggested self-starting charts, which neglect the dynamic pattern of the mean change.

Phase I Distribution-Free Analysis of Multivariate Data

ABSTRACT In this study, a new distribution-free Phase I control chart for retrospectively monitoring multivariate data is developed. The suggested approach, based on the multivariate signed ranks, can be applied to individual or subgrouped data for detection of location shifts with an arbitrary pattern (e.g., isolated, transitory, sustained, progressive, etc.). The procedure is complemented with a LASSO-based post-signal diagnostic method for identification of the shifted variables. A simulation study shows that the method compares favorably with parametric control charts when the process is normally distributed, and largely outperforms other multivariate nonparametric control charts when the process distribution is skewed or heavy-tailed. An R package can be found in the supplementary material.