Singular Integral Equations. By N.I. Muskhelishvili. Translated fromthe 2nd Russian edition by J.R.M. Radok. Pp. 447. F1. 28.50. 1953. (Noordhoff, Groningen)

Pathologic Basis of Diseaseby Stanley L. Robbins is really the fourth edition of hisPathology. Appropriate updating and addition enhance the otherwise identical format, sequence, writing, and illustrations. So many medical students have benefited from this source that it may be the best known general book in the field. I recommend it even more now. Like his former texts, this will be enjoyed for its readability. He clearly lays out a great deal of information. When he includes minutiae, the reasons are clear and one feels that all the material is pertinent. Robbins keeps the whole field in perspective—that is, he does not dwell so long or so heavily on pathologic anatomy or pathogenesis as to tempt the reader to overlook clinical presentation or prognosis. A great strength of the subject of pathology is that it bonds strongly with many other medical sciences and specialties and thus occupies the

Pathologic Basis of Disease

Contents: Variety and Quality. Variability loss and tolerance. Determining tolerances. Tolerance design and experimental design. Offline and online quality control. Parameter design and tolerance design: case study. Experimental design for smaller is better characteristics. Experimental design for larger is better characteristics. Bypassing the S/N ratio: spring experiment. Experimental design for dynamic characteristics.

Introduction to quality engineering. designing quality into products a

Akaike information criterion statistics

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Clinical Prediction Models

A gamma model with a constant coefficient of variation and a long-normal model with constant variance often give similar analyzes of data. In the analysis of data from quality improvement experiments, however, neither the coefficient of variation nor th..

Log normal versus gamma models for analyzing data from quality-improvement experiments

In response surface methodology, rotatability and slope-rotatability are natural and highly desirable properties for second-order regression models. In this paper a measure of robust slope-rotatability for second-order response surface designs with a general correlated error structure is developed and illustrated with different examples for autocorrelated error structure. †This work was partially supported by the second stage Brain Korea 21 Project.

A measure of robust slope-rotatability for second-order response surface experimental designs

In Panda and Das (Cal. Statist. Assoc. Bull., 44, 1994, 83-101) a study of rotatable designs with correlated errors was initiated and a systematic study of first order rotatable designs was attempted. Various correlated structures of the errors were considered. This two-part article relates to a thorough study on robust second order rotatable designs (SORD's) under violation of the usual homoscedasticity assumption of the distribution of errors. Under a suitable autocorrelated structure of the dispersion matrix of the error components, we examine existence and construction of robust rotatable designs. In part I, general conditions for rotatability have been derived and special cases have been examined under autocorrelated structure of the errors. Starting with the usual SORD's (under the uncorrelated error setup), we have discussed a method of construction of SORD's with correlated errors under the autocorrelated structure. An illustrative example is given at the end. In part II,we propose to examine robustness of the usual SORD's with emphasis on properties such as weak rotatability, with due consideration as to the cost involved.

Robust Second Order Rotatable Designs : Part I

In regression models with multiplicative error, estimation is often based on either the log-normal or the gamma model. It is well known that the gamma model with constant coefficient of variation and the log-normal model with constant variance give almost the same analysis. This article focuses on the discrepancies of the regression estimates between the two models based on real examples. It identifies that even though the variance or the coefficient of variation remains constant, but regression estimates may be different between the two models. It also identifies that for the same positive data set, the variance is constant under the log-normal model but non-constant under the gamma model. For this data set, the regression estimates are completely different between the two models. In the process, it explains the causes of discrepancies between the two models.

Rabindra Nath Das

Papers

Log normal versus gamma models for analyzing data from quality-improvement experiments

A measure of robust slope-rotatability for second-order response surface experimental designs

Robust Second Order Rotatable Designs : Part I

Discrepancy in regression estimates between log-normal and gamma: some case studies

Robust Response Surfaces, Regression, and Positive Data Analyses