THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Providing accurate and automated input-modeling support is one of the challenging problems in the application of computer simulation of stochastic systems. The models incorporated in current input-modeling software packages often fall short because they assume independent and identically distributed processes, even though dependent time-series input processes occur naturally in the simulation of many real-life systems. Therefore, this paper introduces a statistical methodology for fitting stochastic models to dependent time-series input processes. Specifically, an automated and statistically valid algorithm is presented to fit autoregressive-to-anything (ARTA) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data. ARTA processes are particularly well suited to driving stochastic simulations. The use of this algorithm is illustrated with examples.

/pdf/fitting-time-series-input-processes-for-simulation-tz20jt0ulu.pdf

Fitting Time-Series Input Processes for Simulation

Conditions required to cause flooding often involve more than one source variable such as large waves combined with a high sea level causing coastal flooding, or high river flow combined with a high sea level causing river flooding. In order to estimate the probability of flooding, one needs to know not only the high and extreme values of each variable, but also their likelihood of occurring simultaneously. This work summarises the terminology and the types of method available for joint probability analysis, and discusses the issues associated with data selection and event definition. It then describes the development and testing of methods for incorporation of temporal dependence into an approach involving Monte Carlo simulation. An illustration study shows the effect of introducing firstly short-term clustering, then seasonality and long-term trend. Three further case studies are used to illustrate some key points in the appropriate use and interpretation of joint probability methods.

Joint probability analysis for estimation of extremes

In most real-world manufacturing systems, the production of goods comprises several autocorrelated stages and the quality characteristics of the goods at each stage are correlated random variables. This paper addresses the problem of monitoring a multivariate–multistage manufacturing process and diagnoses the possible causes of out-of-control signals. To achieve this purpose using multivariate time series models, first a model for the autocorrelated data coming from multivariate–multistage processes is developed. Then, a single neural network is designed, trained and employed to control and classify mean shifts in quality characteristics of all stages. In-control and out-of-control average run lengths and correct classification ratio indices have been chosen to investigate the performance of the designed network. The results of a simulation study show that the network is capable of detecting both in-control and out-of-control signals appropriately.

Designing a multivariate–multistage quality control system using artificial neural networks

https://iris.unito.it/bitstream/2318/157008/1/Angius_Colledani_Horvath_final.pdf

Moments of accumulated reward and completion time in Markovian models with application to unreliable manufacturing systems

Providing accurate and automated input modeling support is one of the challenging problems in the application of computer simulation. The authors present a general-purpose input-modeling tool for representing, fitting, and generating random variates from multivariate input processes to drive computer simulations. We explain the theory underlying the suggested data fitting and data generation techniques, and demonstrate that our framework fits models accurately to both univariate and multivariate input processes.

/pdf/modeling-and-generating-multivariate-time-series-with-3sq4778wbz.pdf

Modeling and generating multivariate time series with arbitrary marginals and autocorrelation structures

Transfer lines simply characterise the interrelationship of manufacturing stages with their buffers and they are used to model the key features of such manufacturing environments with simplifying assumptions. There is a vast literature on these systems, however, little has been done on the transient analysis of the transfer lines by making use of the higher order moments of their performance measures due to the difficulty in determining the evolution of the stochastic processes under consideration. This paper examines the transient behaviour of relatively short transfer lines and derives the distribution of the performance measures of interest. An experiment is designed in order to compare the results of this study with the state-space representations and the simulation. Furthermore, extensions are briefly discussed and directions for future research are suggested.

/pdf/on-the-distribution-of-throughput-of-transfer-lines-2mqpxgpd3l.pdf

On the distribution of throughput of transfer lines

Providing accurate and automated input modeling support is one of the challenging problems in the application of computer simulation. In this paper, we present a general-purpose input-modeling tool for representing, fitting, and generating random variates from multivariate input processes to drive computer simulations. We explain the theory underlying the suggested data fitting and data generation techniques, and demonstrate that our framework fits models accurately to both univariate and multivariate input processes.

Input modeling and its impact: modeling and generating multivariate time series with arbitrary marginals and autocorrelation structures

Providing accurate and automated input modeling support is one of the challenging problems in the application of computer simulation. The models incorporated in current input-modeling software packages often fall short of what is needed because they emphasize independent and identically distributed processes, while dependent multivariate time-series processes occur naturally in the simulation of many real-life systems. We present a model for representing stationary multivariate time-series input processes with marginal distributions from the Johnson translation system and an autocorrelation structure specified through some finite lag. We then describe how to generate data accurately to drive computer simulations. We also introduce a statistical methodology for fitting stochastic models to dependent time-series input processes. Specifically, an automated and statistically valid algorithm is presented to fit ARTA (Autoregressive-to-Anything) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data. We illustrate the use of the data-generation and data-fitting procedures via examples and provide empirical comparisons with some existing input-modeling procedures.

Bahar Deler

Papers

Modeling and generating multivariate time series with arbitrary marginals and autocorrelation structures

On the distribution of throughput of transfer lines

Input modeling and its impact: modeling and generating multivariate time series with arbitrary marginals and autocorrelation structures

A comprehensive input-modeling framework and software for stochastic, discrete-event simulation experiments