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Common risk factors in the returns on stocks and bonds

Eugene F. Fama, Kenneth R. French University of Chicago, Chicago, IL 60637, USA

This paper identifies five common risk factors in the returns on stocks and bonds. There are three stock-market factors: an overall market factor and factors related to firm size and book-to-market equity. There are two bond-market factors, related to maturity and default risks. Stock returns have shared variation due to the stock-market factors, and they are linked to bond returns through shared variation in the bond-market factors. Except for low-grade corporates, the bond-market factors capture the common variation in bond returns. Most important, the five factors seem to explain average returns on stocks and bonds.

New Probabilistic Method for Estimation of Equipment Failures and Development of Replacement Strategies

Miroslav Begovic, Petar Djuric, Joshua Perkel, Branislav Vidakovic, Damir Novose Georgia Institute of Technology, Stony Brook University, KEMA T&D Consulting

When large amount of statistical information about power system component failure rate is available, statistical parametric models can be developed for predictive maintenance. Often times, only partial information is available: installation date and amount, as well as failure and replacement rates. By combining sufficiently large number of yearly populations of the components, estimation of model parameters may be possible. The parametric models may then be used for forecasting of the system’s short term future failure and for formulation of replacement strategies. We employ the Weibull distribution and show how we estimate its parameters from past failure data. Using Monte Carlo simulations, it is possible to assess confidence ranges of the forecasted component performance data.

Cost Analysis with Censored Data

Yijian Huang, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia;

Economic evaluation of medical interventions has become an accepted, and often required, adjunct to the standard effectiveness and safety assessment in clinical research. However, the statistical analysis can be challenging due to censored cost data, as commonly obtained in medical studies. Over the past decade, important statistical issues that arise from censored cost data have been identified and a number of advances made to tackle them. In this article, we will describe these issues, including induced dependent censoring and limited identifiability with the cost distribution, and review recent statistical developments. Available methods address either time-restricted medical cost or lifetime medical cost jointly with survival time. Their applicability and limitation in various practical situations will be discussed.

Address correspondence to: Yijian Huang, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, 1518 Clifton Rd., NE, Atlanta, GA 30322. Phone: (404) 727-2951; Fax: (404) 727-1370;
Email yhuang5@emory.edu