Estimation of seemingly unrelated tobit regressions via the em algorithm

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1987-01-01

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Abstract

An expectation-maximum (EM) likelihood algorithm is used to estimate two seemingly unrelated Tobit regressions in which the dependent variables are truncated normal. An illustrative example on the determination of the life-health insurance and pension benefits is also given. © 1987 American Statistical Association.

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Published Version (Please cite this version)

10.1080/07350015.1987.10509607

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Sloan, FA, C Huang and K Adamache (1987). Estimation of seemingly unrelated tobit regressions via the em algorithm. Journal of Business and Economic Statistics, 5(3). pp. 425–430. 10.1080/07350015.1987.10509607 Retrieved from https://hdl.handle.net/10161/1881.

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Scholars@Duke

Sloan

Frank A. Sloan

J. Alexander McMahon Distinguished Professor Emeritus of Health Policy and Management

Professor Sloan is interested in studying the subjects of health policy and the economics of aging, hospitals, health, pharmaceuticals, and substance abuse. He has received funding from numerous research grants that he earned for studies of which he was the principal investigator. His most recent grants were awarded by the Robert Wood Johnson Foundation, the Center for Disease Control, the Pew Charitable Trust, and the National Institute on Aging. Titles of his projects include, “Why Mature Smokers Do Not Quit,” “Legal and Economic Vulnerabilities of the Master Settlement Agreement,” “Determinants and Cost of Alcohol Abuse Among the Elderly and Near-elderly,” and “Reinsurance Markets and Public Policy.” He received the Investigator Award for his work on the project, “Reoccurring Crises in Medical Malpractice.” Some of his earlier works include the studies entitled, “Policies to Attract Nurses to Underserved Areas,” “The Impact of National Economic Conditions on the Health Care of the Poor-Access,” and “Analysis of Physician Price and Output Decisions.” Professor Sloan’s latest research continues to investigate the trends and repercussions of medical malpractice, physician behavior, and hospital behavior.


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