TY - JOUR
T1 - A new maximum likelihood algorithm for piecewise regression
AU - Tishler, Asher
AU - Zang, Israel
N1 - Funding Information:
* Asher Tishler is a Lecturer, Faculty of Management and Department of Economics, Tel Aviv University, Tel Aviv. Israel. He is currently at the Department of Economics, University of Southern California, Los Angeles, CA. Israel Zang is a Senior Lecturer, Faculty of Management, Tel Aviv University, Tel Aviv, Israel. He is currently Visiting Associate Professor, Faculty of Commerce and Business Administration, The University of British Columbia, Vancouver, Canada. Research was supported by the Israel Institute of Business Research at Tel Aviv University. The authors are indebted to the referees and associate editor and to R. C. Fair, V. Ginsburgh, and M. J. Hinich for valuable comments and to Y. Hoch for programming and testing the algorithm.
PY - 1981/12
Y1 - 1981/12
N2 - This paper presents a piecewise regression method for continuous models containing max or min operators, or both. This method does not require knowledge of the zone in which a shift in regimes occurs. Moreover, it allows the application of analytical derivatives to maximize the likelihood function, which greatly simplifies the estimation of the model. The method proposed exhibits fast convergence and can be used for an arbitrary number of regimes and variables.
AB - This paper presents a piecewise regression method for continuous models containing max or min operators, or both. This method does not require knowledge of the zone in which a shift in regimes occurs. Moreover, it allows the application of analytical derivatives to maximize the likelihood function, which greatly simplifies the estimation of the model. The method proposed exhibits fast convergence and can be used for an arbitrary number of regimes and variables.
KW - Linear and nonlinear regression
KW - Piecewise regression
KW - Regression
UR - http://www.scopus.com/inward/record.url?scp=0001170352&partnerID=8YFLogxK
U2 - 10.1080/01621459.1981.10477752
DO - 10.1080/01621459.1981.10477752
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AN - SCOPUS:0001170352
SN - 0162-1459
VL - 76
SP - 980
EP - 987
JO - Journal of the American Statistical Association
JF - Journal of the American Statistical Association
IS - 376
ER -