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

A review on the influence of drinking water quality towards human health

An adequate supply of safe drinking water is one of the major prerequisites for a healthy life. Inadequate of safe drinking water produce waterborne disease and a major cause of death in many parts of the world, particularly in children. Therefore, it must be treated properly before it can be used and consumed. This chapter provides the guidelines of important parameters for drinking water standard in order to ensure the safeness of drinking water. All the selected parameters were elaborated on the effect of high concentration if human consume the drinking water directly

The effect of autocorrelation on the performance of MEWMA control chart with controlled correlation

Control charts are made to identify assignable causes of difference that could exist in production processes. When traditional control charts are utilized you have the implied presumption that this observations are independently and identically distributed as time passes. It is usually believed the probability distribution which represents the actual observations includes a known functional form and it is constant as time passes. Nevertheless, in reality, observations produced through continuous in addition to discrete generation procedures in many cases are serially correlated. Auto correlation not just breaks the actual independence assumption of conventional control charts, but also can impact the efficiency associated with control charts negatively. In this article, we are going to investigate the result associated with autocorrelation around the performance of MEWMA control chart, in which autocorrelated data were utilized to create the MEWMA chart with induced autocorrelation from various levels of correlations (small, moderate as well as large) and different sample sizes. Simulations had been done to create the data set used to construct the MEWMA control chart and the outcomes implies that all of the control charts constructed had their points outside the designed control limits, that confirmed the effect of autocorrelation to the performance of the MEWMA control chart

PARAMETRIC MODELING AND SIMULATION OF JOINT PRICE-PRODUCTION DISTRIBUTIONS UNDER NON-NORMALITY, AUTOCORRELATION AND HETEROSCEDASTICITY: A TOOL FOR ASSESSING RISK IN AGRICULTURE

This study presents a way to parametrically model and simulate multivariate distributions under potential non-normality, autocorrelation and heteroscedasticity and illustrates its application to agricultural risk analysis. Specifically, the joint probability distribution (pdf) for West Texas irrigated cotton, corn, sorghum, and wheat production and prices is estimated and applied to evaluate the changes in the risk and returns of agricultural production in the region resulting from observed and predicted price and production trends. The estimated pdf allows for time trends on the mean and the variance and varying degrees of autocorrelation and non-normality (kurtosis and right- or left-skewness) in each of the price and production variables. It also allows for any possible price-price, production-production, or price-production correlation.agricultural risk analysis, autocorrelation, heteroscedasticity, multivariate non-normal simulation, West Texas agriculture, Research Methods/ Statistical Methods, Risk and Uncertainty,

Implementing Option Pricing Models When Asset Returns Are Predictable

Option pricing formulas obtained from continuous-time no- arbitrage arguments such as the Black-Scholes formula generally do not depend on the drift term of the underlying asset's diffusion equation. However, the drift is essential for properly implementing such formulas empirically, since the numerical values of the parameters that do appear in the option pricing formula can depend intimately on the drift. In particular, if the underlying asset's returns are predictable, this will influence the theoretical value and the empirical estimate of the diffusion coefficient å. We develop an adjustment to the Black-Scholes formula that accounts for predictability and show that this adjustment can be important even for small levels of predictability, especially for longer-maturity options. We propose a class of continuous-time linear diffusion processes for asset prices that can capture a wider variety of predictability, and provide several numerical examples that illustrate their importance for pricing options and other derivative assets.

Process Control, the Bull Whip Effect and the Supply Chain

The purpose is to introduce the demand for statistical quality control practice in the supply chain environment. We show both the need and application of these measures, especially the need for multivariate quality concepts to reduce the costs of operating supply chains, to control the flow throughout the supply chain and in the dynamic behavior of supply chains to utilize concepts associated with multivariate methods and auto correlated variables. We note that the quality output is as important as the “bull whip” efficiency in the supply chain

Usage of control charts for time series analysis in financial management

We will deal with corporate financial proceeding using statistical process control, specifically time series control charts. The article outlines intersection of two disciplines, namely econometrics and statistical process control. Theoretical part discusses methodology of time series control charts, and in research part, the methodology is demonstrated on two case studies. The first focuses on analysis of Slovak currency from the perspective of its usefulness for generating profits through time series control charts. The second involves regulation of financial flows for a heteroskedastic financial process by EWMA and ARIMA control charts. We use Box-Jenkins methodology to find models of time series of annual Argentinian Gross Domestic Product available as a basic index from 1951-1998. We demonstrate the versatility of control charts not only in manufacturing but also in managing financial stability of cash flows. Specifically, we show their sensitivity in detecting even small shifts in mean which may indicate financial instability. This analytical approach is widely applicable and therefore of theoretical and practical interest

Update and Acceleration of Health Care Using Artificial Intelligence in Medical Treatments and Diagnostics

Researchers support the growth of artificial intelligence and similar methods in health and medical care for the purpose of continuously improving processes. By focusing on the growth on data analytics, statistics, applied mathematics, and computer methods including machine learning, the future of health-care methods will change. The development of computerized methods and the growth of data systems produce ample materials for artificial intelligence to develop and to bring physician assistance programs to enable continuous improvement resulting in superior health and medical care. This includes applications in intensive care as well as diagnostic therapies. The focus is on examples in the use of the promising developments in data science methods, the accumulation of medical and research data. With quality and continuous improvement in process control applications where one determines the usefulness of data analytics, there are great possibilities of change in the improvement in medical applications as well as the management of medical and health-care treatment and diagnostic facilities