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Gerald J. Lieberman

Bio: Gerald J. Lieberman is an academic researcher. The author has contributed to research in topics: Prediction interval & Adaptive control. The author has an hindex of 2, co-authored 2 publications receiving 43 citations.

Papers
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TL;DR: In this paper, the problem of determining the joint prediction interval for the responses at each of K separate settings of the independent variables when all K predictions must be based upon the original fitted model is described.
Abstract: When a linear relationship has been fitted by least squares, the methods for securing a prediction interval for the response at some fixed value of the independent variable are explained in many statistical text books. This paper describes the somewhat more complex problem of determining the joint prediction interval for the responses at each of K separate settings of the independent variables when all K predictions must be based upon the original fitted model.

28 citations

Journal ArticleDOI
TL;DR: In this paper, the present status of statistical process control from the viewpoint of the statistician is described from the point of view of the statistical process controller, and the relationship between the traditional concepts in quality control and the new concept of adaptive control is discussed.
Abstract: This paper describes the present status of statistical process control from the viewpoint of the statistician It summarizes twenty papers dating from early 1930 to the present It indicates the relationship between the traditional concepts in quality control and the new concept of adaptive control

15 citations


Cited by
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TL;DR: The data quality problem in the context of supply chain management (SCM) is introduced and methods for monitoring and controlling data quality are proposed and highlighted.

652 citations

Journal ArticleDOI
TL;DR: In this paper, the authors presented methods for computing three types of simultaneous confidence and prediction intervals (exact, likelihood ratio, and linearized) on output from nonlinear regression models with normally distributed residuals.
Abstract: Methods are presented for computing three types of simultaneous confidence and prediction intervals (exact, likelihood ratio, and linearized) on output from nonlinear regression models with normally distributed residuals. The confidence intervals can be placed on individual regression parameters or on the true regression function at any number of points in the domain of the independent variables, and the prediction intervals can be placed on any number of future observations. The confidence intervals are analogous to simultaneous Scheffe intervals for linear models and the prediction intervals are analogous to the prediction intervals of Hahn (1972). All three types of intervals can be computed efficiently by using the same straightforward Lagrangian optimization scheme. The prediction intervals can be treated in the same computational framework as the confidence intervals by including the random errors as pseudoparameters in the Lagrangian scheme. The methods are applied to a hypothetical groundwater model for flow to a well penetrating a leaky aquifer. Three different data sets are used to demonstrate the effect of sampling strategies on the intervals. For all three data sets, the linearized confidence intervals are inferior to the exact and likelihood ratio intervals, with the latter two being very similar; however, all three types of prediction intervals yielded similar results. The third data set (time drawdown data at only a single observation well) points out many of the problems that can arise from extreme nonlinear behavior of the regression model.

133 citations

Journal ArticleDOI
TL;DR: In this article, a review of known results on prediction intervals for univariate distributions is presented, including results for parametric continuous and discrete distributions as well as those based on distribution-free methods.
Abstract: This review covers some known results on prediction intervals for univariate distributions. Results for parametric continuous and discrete distributions as well as those based on distribution-free methods are included. Prediction intervals based on Bayesian and sequential methods are not covered. Methods of construction of prediction intervals and other related problems are discussed.

123 citations