A Joint Regression Variable and Autoregressive Order Selection Criterion

Abstract.  In linear regression models with autocorrelated errors, we apply the residual likelihood approach to obtain a residual information criterion (RIC), which can jointly select regression variables and autoregressive orders. We show that RIC is a consistent criterion. In addition, our simulation studies indicate that it outperforms heuristic selection criteria – the Akaike information criterion and the Bayesian information criterion – when the signal-to-noise ratio is not weak.     

结合模型混淆度和BIC准则的语种识别精细建模方法

提出了一种基于语种模型混淆度的模型参数估计方法,并结合贝叶斯信息准则(Bayesian information criterion,BIC)来进行模型的选取,避免了大量标注信息的需求.在NIST-07语种识别30,10和3s的测试任务中,分别给出了在最大似然(Maximum likelihood,ML)准则和最大互信息...     

Improved AIC selection strategy for survival analysis

In survival analysis, it is of interest to appropriately select significant predictors. In this paper, we extend the AIC_C selection procedure of Hurvich and Tsai to survival models to improve the traditional AIC for small sample sizes. A theoretical verification under a special case of the exponential distribution is provided. Simulation studies illustrate that the proposed method substantially outperforms its counterpart: AIC, in small samples, and competes it in moderate and large samples. Two real data sets are also analyzed.     

Locating multiple interacting quantitative trait loci using robust model selection

One of the most popular criteria for model selection is the Bayesian Information Criterion (BIC). It is based on an asymptotic approximation using Bayes rule when the sample size tends to infinity and the dimension of the model is fixed. Although it works well in classical applications, it performs less satisfactorily for high dimensional problems, i.e. when the number of regressors is very large compared to the sample size. For this reason, an alternative version of the BIC has been proposed for the problem of mapping quantitative trait loci (QTLs) considered in genetics. One approach is to locate QTLs by using model selection in the context of a regression model with an extremely large number of potential regressors. Since the assumption of normally distributed errors is often unrealistic in such settings, we extend the idea underlying the modified BIC to the context of robust regression.     

BIC-based node order learning for improving Bayesian network structure learning

Node order is one of the most important factors in learning the structure of a Bayesian network (BN) for probabilistic reasoning. To improve the BN structure learning, we propose a node order learning algorithmbased on the frequently used Bayesian information criterion (BIC) score function. The algorithm dramatically reduces the space of node order and makes the results of BN learning more stable and effective. Specifically, we first find the most dependent node for each individual node, prove analytically that the dependencies are undirected, and then construct undirected subgraphs U_G. Secondly, the U_G- is examined and connected into a single undirected graph U_GC. The relation between the subgraph number and the node number is analyzed. Thirdly, we provide the rules of orienting directions for all edges in UGC, which converts it into a directed acyclic graph (DAG). Further, we rank the DAG’s topology order and describe the BIC-based node order learning algorithm. Its complexity analysis shows that the algorithm can be conducted in linear time with respect to the number of samples, and in polynomial time with respect to the number of variables. Finally, experimental results demonstrate significant performance improvement by comparing with other methods.     

广播语音的音频分割

本文的广播电视新闻的分割系统分为三部分:分割、分类和聚类。分割部分是采用本文提出的基于检测熵变化趋势的分割算法来检测连续语音音频信号的声学特征跳变点,从而实现不同性质的音频信号的分割。这种检测方法不同于传统的需要门限的跳变点检测方法,它是以检测一定窗长的信号内部的每一个可能的分割点所分割的两段信号的信号熵的变化趋势来检测音频信号声学特征跳变点的,可以避免由于门限的选择不当所带来的分割错误。分类部分是采用传统的基于高斯混合模型(GMM)的高斯分类器进行分类,聚类部分采用基于矢量量化(VQ)的说话人聚类算法进行说话人聚类。应用此系统分割三段30分钟的新闻,成功的实现了连续音频信号的分割,去除掉了所有的背景音乐,以较高的精度把属于同一个人的说话语音划归为一类,为广播语音的分类识别打下了良好的基础。     

基于BIC的语音识别模型压缩算法

当对HMM(Hidden Markov Model,隐马尔科夫模型)语音模型进行GMM(Gaussian Mixture Model,混合高斯模型)区分训练增加组件时,语音模型的识别率会随着GMM的组件增多而增加,模型的大小也会增加,这就造成了语音模型的臃肿。而在移动端使用本地语音模型进行识别时,存放一个几百兆的模型很不合适。针对上述问题,本文提出将一个GMM组件数较多的语音模型利用BIC准则压缩到指定的组件数,从而在模型大小合适的情况下尽量保证模型的识别率。实验结果表明,使用本方法进行压缩之后的语音识别率比未压缩的相同组件数的语音识别模型的识别率要高。     

基于贝叶斯信息准则的文本主题数估计

特定领域的主题识别和关键词提取有着广泛的应用,但通过人工指定识别或文本聚类自动生成的主题类别缺乏客观的度量方法。该文结合基于BIC准则的模型选择理论和独立分量分析技术对主题的数量进行概率估计,给出主题数量在BIC意义下的统计分布。在此基础上实现了文档矩阵的ICA分解,并根据分离的独立分量获得主题的关键词及其权重。实验表明,该方法在没有领域知识支持的情况下能估计出反映文本集合的主题数并提取相应的关键词。     

基于非线性贝叶斯理论和BIC准则的防渗墙高精度瑞雷波反演研究

横波速度和厚度的反演精度是决定瑞雷波法能否应用于防渗墙检测的关键因素,同时也是瑞雷波反演的难点。为获得高精度的防渗墙横波速度和厚度,采用BIC准则和非线性贝叶斯理论,通过理论模型与实测数据相结合的方式,对与防渗墙结构相似的三层含硬夹层模型和防渗墙瑞雷波数据进行反演,并将反演所确定的模型与已知模型进行对比分析,从而验证反演方法的有效性和准确性。结果表明:非线性贝叶斯方法能够有效优化硬夹层模型和防渗墙数据,通过频散曲线反演,能够获得高精度的横波速度和厚度反演结果;将BIC准则应用到硬夹层模型和防渗墙数据,确定硬夹层和防渗墙最佳参数化模型分别为3层和8层,该模型与实际情况最为吻合,频散曲线反演精度最高。研究成果对提高防渗墙无损检测精度、确保堤坝安全运行具有重要意义。     

ARMAX模型的阶估计

本文给出推广的BIC准则,讨论了ARMAX模型的阶估计收敛性和模型中含未建模时,阶估计的鲁棒性。