BOOK REVIEW: Regression Models: Censored, Sample-selected, or Truncated Data. Richard Breen, Sage...
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2. REGRESSION MODELS: CENSORED, SAMPLE- SELECTED, OR TRUNCATED DATA. Richard Breen, Sage University Paper series on Quantitative Ap- plications in the Social Sciences, series no. 07-111, Thousand Oaks, 1996. No. of pages: viii#80. Price: £7.95. ISBN: 0-8039-5710-6 This book provides an easily understood introduc- tion to regression models for censored, sample- selected and truncated data. During the last two decades such models have come to be very widely used in the social sciences. The book is written for social scientists, but it may also be relevant for epidemiologists and medical researchers working with non-experimental data. In non-experimental research, it is often the case that an outcome vari- able for a sample is not representative of the popu- lation for whom you would like to generalize the results. A set of techniques that can be used in this case are introduced in this small book. The book consists of five chapters starting with the simplest model and then gradually building up to- wards more advanced issues. The first chapter gives an introduction to the problems of censoring, sample selection and truncation, and the next two chapters go through the basic models for data with these features. Chapter 4 contains some extensions of the basic models, while Chapter 5 focuses on some of the criticisms that have been made of these approaches and the difficulties associated with them. In addition, there are two appendices on technical details. The book assumes as its point of departure that the intended readers know the OLS (ordinary least squares) method for regression models, but nothing further than that. Therefore, it includes a section on the maximum likelihood method for readers unfam- iliar with this method. Furthermore, the book takes the reader through the basics of the Normal distri- bution. As can be gathered from this, it really does not require many prerequisites of its readers. One of the strengths of this book is that it is filled with examples which help the reader to clar- ify the concepts and to relate the theoretical mod- els to applications. Some of these examples are continued throughout the book. The literature in this area is very inconsistent as regards terminol- ogy, but the concepts are explained clearly in Richard Breen’s book, with the help of the exam- ples. Breen also emphasizes the guidelines for the practical application of the models. He does this, in part, by including sub-sections on the interpreta- tion of the parameters for each model, where nice intuitive explanations are given. The final chapter of the book contains a very useful overview of some possible pitfalls and methods to avoid them. One thing that I particularly like about the book is Richard Breen’s strong advocacy of the maximum likelihood method. As he mentions several times in the book, the maximum likelihood method is now readily available in many computer programs and there is nothing to be gained from choosing a two- step estimation method rather than the maximum likelihood method. On the contrary, maximum like- lihood estimators have desirable properties that two-step estimators often do not share. Unfortunately, the author introduces some con- fusion, especially for the inexperienced reader, by attempting to deal with a general censoring or trunc- ation limit, c. The problem is that apparently he wants to avoid it in the notation and in the formulae, but it is not clear when c is assumed to be equal to 0, and when it is not! In fact, some of the formulae given in the book are incorrect. Also, there are some other typographical errors and minor inaccuracies in some of the formulae. In a book at this level such things should be avoided at almost any cost. Another minor complaint about the book is that the notes are given at the end of each chapter, which is not very handy. I also miss an index, which would be very useful in a book like this. All things considered, the book is recommen- dable to students and researchers alike. It provides a very nice introduction to this area, with the provisos already mentioned. I even think that it may be worthwhile for people who already know and use the models to invest the time in reading the final chapter with the caveat emptor. PETER JENSEN Centre for Labour Market and Social Research Gustav Wieds Vej 10C DK-8000 Aarhus C, Denmark 3. STATISTICAL APPLICATIONS USING FUZZY SETS. K. G. Manton, M. A. Woodbury and H. D. Tolley, Wiley, New York, 1994. No. of pages: xiv#312. Price: £53. ISBN: 0-471-54561-9 The idea of fuzzy sets originated in electrical engin- eering and has also been used in computer science, physics and theoretical biology. As with neural networks, in some respects this approach might be seen as a competitor to statistics. However, only a few scattered articles are available in the statist- ical literature. Now this book, by Manton, Wood- bury and Tolley, should make these methods more widely accessible. BOOK REVIEWS 1789 ( 1997 by John Wiley & Sons, Ltd. Statist. Med., Vol. 16, 1787—1790 (1997)
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Transcript of BOOK REVIEW: Regression Models: Censored, Sample-selected, or Truncated Data. Richard Breen, Sage...
2. REGRESSION MODELS: CENSORED, SAMPLE-
SELECTED, OR TRUNCATED DATA. Richard Breen,Sage University Paper series on Quantitative Ap-plications in the Social Sciences, series no. 07-111,Thousand Oaks, 1996. No. of pages: viii#80.Price: £7.95. ISBN: 0-8039-5710-6
This book provides an easily understood introduc-tion to regression models for censored, sample-selected and truncated data. During the last twodecades such models have come to be very widelyused in the social sciences. The book is written forsocial scientists, but it may also be relevant forepidemiologists and medical researchers workingwith non-experimental data. In non-experimentalresearch, it is often the case that an outcome vari-able for a sample is not representative of the popu-lation for whom you would like to generalize theresults. A set of techniques that can be used in thiscase are introduced in this small book.
The book consists of five chapters starting with thesimplest model and then gradually building up to-wards more advanced issues. The first chapter givesan introduction to the problems of censoring, sampleselection and truncation, and the next two chaptersgo through the basic models for data with thesefeatures. Chapter 4 contains some extensions of thebasic models, while Chapter 5 focuses on some of thecriticisms that have been made of these approachesand the difficulties associated with them. In addition,there are two appendices on technical details.
The book assumes as its point of departure thatthe intended readers know the OLS (ordinary leastsquares) method for regression models, but nothingfurther than that. Therefore, it includes a section onthe maximum likelihood method for readers unfam-iliar with this method. Furthermore, the book takesthe reader through the basics of the Normal distri-bution. As can be gathered from this, it really doesnot require many prerequisites of its readers.
One of the strengths of this book is that it isfilled with examples which help the reader to clar-ify the concepts and to relate the theoretical mod-els to applications. Some of these examples arecontinued throughout the book. The literature inthis area is very inconsistent as regards terminol-
ogy, but the concepts are explained clearly inRichard Breen’s book, with the help of the exam-ples. Breen also emphasizes the guidelines for thepractical application of the models. He does this, inpart, by including sub-sections on the interpreta-tion of the parameters for each model, where niceintuitive explanations are given. The final chapterof the book contains a very useful overview ofsome possible pitfalls and methods to avoid them.
One thing that I particularly like about the bookis Richard Breen’s strong advocacy of the maximumlikelihood method. As he mentions several times inthe book, the maximum likelihood method is nowreadily available in many computer programs andthere is nothing to be gained from choosing a two-step estimation method rather than the maximumlikelihood method. On the contrary, maximum like-lihood estimators have desirable properties thattwo-step estimators often do not share.
Unfortunately, the author introduces some con-fusion, especially for the inexperienced reader, byattempting to deal with a general censoring or trunc-ation limit, c. The problem is that apparently hewants to avoid it in the notation and in the formulae,but it is not clear when c is assumed to be equal to 0,and when it is not! In fact, some of the formulae givenin the book are incorrect. Also, there are some othertypographical errors and minor inaccuracies in someof the formulae. In a book at this level such thingsshould be avoided at almost any cost.
Another minor complaint about the book is thatthe notes are given at the end of each chapter,which is not very handy. I also miss an index,which would be very useful in a book like this.
All things considered, the book is recommen-dable to students and researchers alike. It providesa very nice introduction to this area, with theprovisos already mentioned. I even think that itmay be worthwhile for people who already knowand use the models to invest the time in reading thefinal chapter with the caveat emptor.
PETER JENSEN
Centre for Labour Market and Social ResearchGustav Wieds Vej 10C
DK-8000 Aarhus C, Denmark
3. STATISTICAL APPLICATIONS USING FUZZY SETS.K. G. Manton, M. A. Woodbury and H. D. Tolley,Wiley, New York, 1994. No. of pages: xiv#312.Price: £53. ISBN: 0-471-54561-9
The idea of fuzzy sets originated in electrical engin-eering and has also been used in computer science,
physics and theoretical biology. As with neuralnetworks, in some respects this approach might beseen as a competitor to statistics. However, onlya few scattered articles are available in the statist-ical literature. Now this book, by Manton, Wood-bury and Tolley, should make these methods morewidely accessible.
BOOK REVIEWS 1789
( 1997 by John Wiley & Sons, Ltd. Statist. Med., Vol. 16, 1787—1790 (1997)
