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Estimation in hazard regression models under ordered departures from proportionality

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  • Bhattacharjee, Arnab

Abstract

Notions of monotone ordering with respect to continuous covariates in duration data regression models have recently been discussed, and tests for the proportional hazards model against such alternatives have been developed (Bhattacharjee and Das, 2002). Such monotone/ ordered departures are common in applications, and provide useful additional information about the nature of covariate dependence. In this paper, we describe methods for estimating hazard regression models when such monotone departures are known to hold. In particular, it is shown how the histogram sieve estimators (Murphy and Sen, 1991) in this setup can be smoothed and order restricted estimation performed using biased bootstrap techniques like adaptive bandwidth kernel estimators (Brockmann et. al., 1993; Schucany, 1995) or data tilting (Hall and Huang, 2001). The performance of the methods is compared using simulated data, and their use is illustrated with applications from biomedicine and economic duration data.
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  • Bhattacharjee, Arnab, 2004. "Estimation in hazard regression models under ordered departures from proportionality," Computational Statistics & Data Analysis, Elsevier, vol. 47(3), pages 517-536, October.
  • Handle: RePEc:eee:csdana:v:47:y:2004:i:3:p:517-536
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    1. Enno MAMMEN & C. THOMAS-AGNAN, 1996. "Smoothing Splines And Shape Restrictions," SFB 373 Discussion Papers 1996,87, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    2. Hall, Peter & Turlach, Berwin A., 1999. "Reducing bias in curve estimation by use of weights," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 67-86, March.
    3. E. Mammen, 1999. "Smoothing Splines and Shape Restrictions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 26(2), pages 239-252.
    4. Li, Yi-Hwei & Klein, John P. & Moeschberger, M. L., 1996. "Effects of model misspecification in estimating covariate effects in survival analysis for small sample sizes," Computational Statistics & Data Analysis, Elsevier, vol. 22(2), pages 177-192, July.
    5. Zongwu Cai, 2003. "Local Linear Estimation for Time-Dependent Coefficients in Cox's Regression Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 93-111.
    6. Bhattacharjee, A. & Samarjit Das, 2002. "Testing Proportionality in Duration Models with Respect to Continuous Covariates," Cambridge Working Papers in Economics 0220, Faculty of Economics, University of Cambridge.
    7. Farmen, Mark & Marron, J. S., 1999. "An assessment of finite sample performance of adaptive methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 30(2), pages 143-168, April.
    8. Lee, Thomas C. M., 2003. "Smoothing parameter selection for smoothing splines: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 139-148, February.
    9. Murphy, S. A. & Sen, P. K., 1991. "Time-dependent coefficients in a Cox-type regression model," Stochastic Processes and their Applications, Elsevier, vol. 39(1), pages 153-180, October.
    10. Fischer, N. I. & Mammen, E. & Marron, J. S., 1994. "Testing for multimodality," Computational Statistics & Data Analysis, Elsevier, vol. 18(5), pages 499-512, December.
    11. Kathryn A. Prewitt, 2003. "Efficient Bandwidth Selection in Non-parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 75-92.
    12. Torben Martinussen, 2002. "Efficient Estimation of Fixed and Time-varying Covariate Effects in Multiplicative Intensity Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(1), pages 57-74.
    13. A. Bhattacharjee & C. Higson & S. Holly & P. Kattuman, 2009. "Macroeconomic Instability and Business Exit: Determinants of Failures and Acquisitions of UK Firms," Economica, London School of Economics and Political Science, vol. 76(301), pages 108-131, February.
    14. P. Hall & B. Presnell, 1999. "Intentionally biased bootstrap methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 143-158.
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    Citations

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    Cited by:

    1. Bhattacharjee, Arnab, 2004. "A Simple Test for the Absence of Covariate Dependence in Hazard Regression Models," MPRA Paper 3937, University Library of Munich, Germany.
    2. Bhattacharjee, Arnab & Bhattacharjee, Madhuchhanda, 2007. "Bayesian Analysis of Hazard Regression Models under Order Restrictions on Covariate Effects and Ageing," MPRA Paper 3938, University Library of Munich, Germany.
    3. Bhattacharjee, A. & Higson, C. & Holly, S. & Kattuman, P., 2004. "Business Failure in UK and US Quoted Firms: Impact of Macroeconomic Instability and the Role of Legal Institutions," Cambridge Working Papers in Economics 0420, Faculty of Economics, University of Cambridge.
    4. A. Bhattacharjee & C. Higson & S. Holly & P. Kattuman, 2009. "Macroeconomic Instability and Business Exit: Determinants of Failures and Acquisitions of UK Firms," Economica, London School of Economics and Political Science, vol. 76(301), pages 108-131, February.
    5. Orbe, Jesus & Nunez-Anton, Vicente, 2006. "Alternative approaches to study lifetime data under different scenarios: from the PH to the modified semiparametric AFT model," Computational Statistics & Data Analysis, Elsevier, vol. 50(6), pages 1565-1582, March.
    6. Arnab Bhattacharjee, 2009. "Testing for Proportional Hazards with Unrestricted Univariate Unobserved Heterogeneity," Discussion Paper Series, Department of Economics 200904, Department of Economics, University of St. Andrews.
    7. Bhattacharjee, Arnab & Hany, Jie, 2010. "Financial Distress in Chinese Industry: Microeconomic, Macroeconomic and Institutional Infuences," SIRE Discussion Papers 2010-53, Scottish Institute for Research in Economics (SIRE).
    8. Arnab Bhattacharjee, 2008. "Partial Orders with Respect to Continuous Covariates and Tests for the Proportional Hazards Model," Discussion Paper Series, Department of Economics 200807, Department of Economics, University of St. Andrews.
    9. Arnab Bhattacharjee, 2005. "Models of Firm Dynamics and the Hazard Rate of Exits: Reconciling Theory and Evidence using Hazard Regression Models," Econometrics 0503021, EconWPA.

    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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