Predicting the conditional probability of discovering a new class
Predicting the conditional probability of discovering a new class
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DOI：
10.1198/016214504000001709
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发表时间：
2004-12-01
影响因子：
3.7
通讯作者：
Mao, CX
中科院分区：
文献类型：
--
作者：
Mao, CX
考虑一个由不相交类组成的种群。从各个领域产生的一个重要问题是预测发现一个新类的随机条件概率。发现概率的渐近正态性是在泊松模型中建立的，其中来自每个类的个体的数量是具有类特异性比率的泊松过程。一个新的推导是著名的古德-图尔敏预测作为一个基于矩的估计发现概率的渐近极限。Good-Toulmin预测也被证明是一个非参数的经验贝叶斯估计的发现概率的期望给出的泊松过程和一个近似的无偏估计的发现概率的期望在多项式模型中的可识别的部分。矩估计的性质进行了研究，使置信区间和预测区间，可以构建，良好的Toulmin预测和发现概率被证明具有非负相关。一个条件的非参数最大似然估计开发作为一种替代的时刻为基础的估计。作为应用，该方法用于预测在基因组测序实验中从表达的序列标签发现新基因的概率。
Consider a population comprising disjoint classes. An important problem arising from various fields is prediction of the random conditional probability of discovering a new class. The asymptotic normality of the discovery probability is established in a Poisson model, where the number of individuals from each class is a Poisson process with a class-specific rate. A new derivation is presented for the well-known Good-Toulmin predictor as a moment-based estimator for the asymptotic limit of the discovery probability. The Good-Toulmin predictor is also shown to be a nonparametric empirical Bayes estimator for the expectation of the discovery probability given the rates of the Poisson processes and an approximation to an unbiased estimator only for the identifiable part of the expectation of the discovery probability in a multinomial model. The properties of the moment based estimator are investigated so that confidence and prediction intervals, can be constructed, The Good-Toulmin predictor and the discovery probability are shown to have a nonnegative correlation. A conditional nonparametric maximum likelihood estimator is developed as an alternative to the moment-based estimator. As an application, the methods are used to predict the probability of discovering a new gene from expressed sequence tags in a genomic sequencing experiment.