Maximum likelihood estimation for conditional distribution single-index models under censoring
Maximum likelihood estimation for conditional distribution single-index models under censoring
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DOI：
10.1016/j.jmva.2012.07.012
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发表时间：
2013-02
期刊：
影响因子：
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通讯作者：
E. Strzalkowska-Kominiak;R. Cao
中科院分区：
文献类型：
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作者：
E. Strzalkowska-Kominiak;R. Cao
针对截尾条件分布或密度的半参数估计问题，提出了一种新的似然方法。证明了单指数模型中参数向量的两种最大似然估计的相合性和渐近正态性。所考虑的单指数模型可以被视为信用评分和估计信用风险中违约概率的有用工具。提出了一种数据驱动的带宽选择方法。它允许选择我们的方法中涉及的平滑参数。通过仿真研究了估计量的有限样本性能，并与Bouaziz和Lopez(2010)[1]提出的方法进行了比较。据我们所知，这是这方面唯一存在的竞争对手。仿真研究表明，该方法具有良好的性能。
A new likelihood approach is proposed for the problem of semiparametric estimation of a conditional distribution or density under censoring. Consistency and asymptotic normality for two versions of the maximum likelihood estimator of the parameter vector in the single index model are proved. The single-index model considered can be seen as a useful tool for credit scoring and estimation of the default probability in credit risk. A data-driven bandwidth selection procedure is proposed. It allows to choose the smoothing parameter involved in our approach. The finite sample performance of the estimators has been studied by simulations, where the new method has been compared with the method proposed by Bouaziz and Lopez (2010) [1]. To the best of our knowledge this is the only existing competitor in this context. The simulation study shows the good behavior of the proposed method.