水文序列相依变异识别的RIC定阶准则——以自回归模型为例

水文过程相依性是水文变异的主要表现形式之一,应用自回归模型对其进行拟合时合理确定模型阶数是一个难点问题。本文在分析AIC和BIC准则的基础上,提出了一种以原序列与其相依成分的相关系数作为拟合度指标,同时借用信息熵形式的函数式,作为模型不确定性度量指标的自回归模型定阶准则(简称RIC准则)。以AR(1)、AR(2)、AR(3)和AR(4)模型为例进行统计试验,将不同序列长度下该准则的定阶准确率与其他定阶准则进行比较,试验结果表明,RIC准则对于上述模型均具有较好的适应性,且定阶准确率远高于AIC准则,其中对于前三阶模型RIC准则优于BIC准则,但四阶模型略低于BIC准则。RIC准则的优势是可以同时满足模型定阶、相依程度分级与模型检验的需求,将其应用于实测水文序列分析,结果显示,该准则能较准确地识别自回归模型的阶数,且符合提出的"相依有变异而残差无变异的最小阶数"的检验标准。     

基于CCS和DSK5402板的音频采集和回放程序设计

为了给程序设计作下基础,本文首先介绍了TI公司的TMS320VC5402和AIC(模拟接口电路)芯片TLC320AD50C的特点,最后着重介绍了利用DSK板上的TMS320C5402和TLC320AD50C实现音频采集并实时回放的软件设计过程,并利用CCS进行了模拟.     

基于非线性规划的指数自回归模型的辨识

讨论了指数自回归模型的辨识问题，证明了该模型最小二乘估计的目标函数的非凸性，并给出了使该函数为凸的条件，最后给出了辨识该模型的算法及该算法的收敛性，并以数值例子加以说明。     

Exploring Particle Density Effects on Partial Mobility of Steelhead Spawning Gravels

Yellowbank Creek is a small stream in coastal central California being assessed for salmonid habitat limiting factors and restoration potential. Yellowbank flows through low‐density marine mudstone bedrock, which is the gravel source for the stream. To assess the potential effects of the low‐density substrate on spawning gravels, a tracer stone study comparing the incipient motion of low‐density mudstone particles and typical density granitic particles was used to populate a logistic regression particle entrainment model. A model comparison approach was used to test the strength of the model. Results demonstrate partial mobility of both mudstone and granitic particles under boundary shear conditions ranging from 6.9 to 42.2 N m^‐2. The modelling results quantify the strong negative correlation between particle entrainment and particle density. Mudstone gravel was three times more likely to be entrained than granitic gravel, within the context of the experimental conditions. The effect of density difference on partial mobility was greater in smaller grain size fractions. This work has implications for salmonid spawning success in atypical geologic settings and may assist in prioritization of restoration efforts. Copyright © 2013 John Wiley & Sons, Ltd.     

Defect Prediction Using Akaike and Bayesian Information Criterion

Data available in software engineering for many applications contains variability and it is not possible to say which variable helps in the process of the prediction. Most of the work present in software defect prediction is focused on the selection of best prediction techniques. For this purpose, deep learning and ensemble models have shown promising results. In contrast, there are very few researches that deals with cleaning the training data and selection of best parameter values from the data. Sometimes data available for training the models have high variability and this variability may cause a decrease in model accuracy. To deal with this problem we used the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) for selection of the best variables to train the model. A simple ANN model with one input, one output and two hidden layers was used for the training instead of a very deep and complex model. AIC and BIC values are calculated and combination for minimum AIC and BIC values to be selected for the best model. At first, variables were narrowed down to a smaller number using correlation values. Then subsets for all the possible variable combinations were formed. In the end, an artificial neural network (ANN) model was trained for each subset and the best model was selected on the basis of the smallest AIC and BIC value. It was found that combination of only two variables’ ns and entropy are best for software defect prediction as it gives minimum AIC and BIC values. While, nm and npt is the worst combination and gives maximum AIC and BIC values.     

Model selection for zero-inflated regression with missing covariates

Count data are widely existed in the fields of medical trials, public health, surveys and environmental studies. In analyzing count data, it is important to find out whether the zero-inflation exists or not and how to select the most suitable model. However, the classic AIC criterion for model selection is invalid when the observations are missing. In this paper, we develop a new model selection criterion in line with AIC for the zero-inflated regression models with missing covariates. This method is a modified version of Monte Carlo EM algorithm which is based on the data augmentation scheme. One of the main attractions of this new method is that it is applicable for comparison of candidate models regardless of whether there are missing data or not. What is more, it is very simple to compute as it is just a by-product of Monte Carlo EM algorithm when the estimations of parameters are obtained. A simulation study and a real example are used to illustrate the proposed methodologies.     

Information methods for model selection in linear mixed effects models with application to HCV data

In this paper, we derive a small sample Akaike information criterion, based on the maximized loglikelihood, and a small sample information criterion based on the maximized restricted loglikelihood in the linear mixed effects model when the covariance matrix of the random effects is known. Small sample corrected information criteria are proposed for a special case of linear mixed effects models, the balanced random-coefficient model, without assuming the random coefficients covariance matrix to be known. A simulation study comparing the derived criteria and several others for model selection in the linear mixed effects models is presented. We illustrate the behavior of the studied information criteria on real data from a study of subjects coinfected with HIV and Hepatitis C virus. Robustness of the criteria, in terms of the error distributed as a mixture of normal distributions, is also studied. Special attention is given to the behavior of the conditional AIC by Vaida and Blanchard (2005). Among the studied criteria, GIC performs best, while cAIC exhibits poor performance. Because of its inferior performance, as demonstrated in this work, we do not recommend its use for model selection in linear mixed effects models.     

TMS320C6713与TLV320AIC23B的接口设计及实现

根据TI公司的IMS320C6713多通道缓冲串口(McBSP)和音频解码芯片AIC23B的工作原理,设计了音频解码电路.将TMS320C6713多通道缓冲串口直接与AIC23B相连,其优点是操作简单,不占用处理器的总线,不影响其他功能模块的性能.给出了TMS320C6713和AIC23B的接口电路和软件编程实现.     

Automatic transaction of signal via statistical modeling

The statistical information processing can be characterized by the likelihood function defined by giving an explicit form for an approximation to the true distribution. This mathematical representation, which is usually called a model, is built based on not only the current data but also prior knowledge on the object and the objective of the analysis. Akaike^2,3) showed that the log-likelihood can be considered as an estimate of the Kullback-Leibler (K-L) information which measures the similarity between the predictive distribution of the model and the true distribution. Akaike information criterion (AIC) is an estimate of the K-L information and makes it possible to evaluate and compare the goodness of many models objectively. In consequence, the minimum AIC procedure allows us to develop automatic modeling and signal extraction procedures. In this article, we give a simple explanation of statistical modeling based on the AIC and demonstrate four examples of applying the minimum AIC procedure to an automatic transaction of signals observed in the earth sciences. Genshiro, Kitagawa, Ph.D.: He is a Professor in the Department of Prediction and Control at the Institute of Statistical Mathematics. He is currently Deputy Director of the Institute of Statistical Mathematics and Professor of Statistical Science at the Graduate University for Advanced Study. He obtained his Ph.D. from the Kyushu University in 1983. His primary research interests are in time series analysis, non-Gaussian nonlinear filtering, and statistical modeling. He has published over 50 research papers. He was awarded the 2nd Japan Statistical Society Prize in 1997. Tomoyuki Higuchi, Ph.D.: He is an Associate Professor in the Department of Prediction and Control at the Institute of Statistical Mathematics. He is currently an Associate Professor of Statistical Science at the Graduate University for Advanced Study. He obtained his Ph.D. from the University of Tokyo in 1989. His research interests are in statistical modeling of space-time data, stochastic optimization techniques, and data mining. He has published over 30 research papers.     

Detecting change-points in Markov chains

Markov chains provide a flexible model for dependent random variables with applications in such disciplines as physics, environmental science and economics. In the applied study of Markov chains, it may be of interest to assess whether the transition probability matrix changes during an observed realization of the process. If such changes occur, it would be of interest to estimate the transitions where the changes take place and the probability transition matrix before and after each change. For the case when the number of changes is known, standard likelihood theory is developed to address this problem. The bootstrap is used to aid in the computation of p-values. When the number of changes is unknown, the AIC and BIC measures are used for model selection. The proposed methods are studied empirically and are applied to example sets of data.