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Approximate Representation of Estimators in Constrained Regression Problems

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  • Jinde Wang

Abstract

The estimators of inequality‐constrained regression problems can be computed by iterative algorithms of mathematical programming, but they do not have analytical expressions in terms of the given data. This situation brings obstacles to further studies on the constrained regression. In this paper we derive approximate representations of the estimators with a remainder of magnitude (N−1 log log N)1/2. From these representations one can clearly see the concrete structure of the estimators of these problems. It will be very helpful for further regression analysis.

Suggested Citation

  • Jinde Wang, 2000. "Approximate Representation of Estimators in Constrained Regression Problems," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 27(1), pages 21-33, March.
    • Handle: RePEc:bla:scjsta:v:27:y:2000:i:1:p:21-33
    DOI: 10.1111/1467-9469.00175
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    Cited by:

    1. Deng, Lifeng & Ding, Jieli & Liu, Yanyan & Wei, Chengdong, 2018. "Regression analysis for the proportional hazards model with parameter constraints under case-cohort design," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 194-206.
    2. Li, Yifan & Nolte, Ingmar & Pham, Manh Cuong, 2024. "Parametric risk-neutral density estimation via finite lognormal-Weibull mixtures," Journal of Econometrics, Elsevier, vol. 241(2).
    3. Ding, Jieli & Tian, Guo-Liang & Yuen, Kam Chuen, 2015. "A new MM algorithm for constrained estimation in the proportional hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 135-151.

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