基于递归算法复杂度分析的教学探讨

递归算法的复杂度一直是《数据结构》课程的的难点和教学重点,由于递归算法本身的特殊性,通常无法通过分析语句执行频度来计算其复杂度,给教学带来困难,为此,提出一种依据递归算法的特点建立算法模型,列出语句执行频度方程,引入差分方程,采用数学迭代法求解来组织教学。     

基于R软件rpart包的分类与回归树应用

对于许多分类和回归问题,二叉树（Binary Tree）提供了有趣而又形象化的方式来研究数据,它主要是按照一定的规则拆分自变量,而完成对因变量的合理分类,进一步可以对未知分类进行预测。在主要介绍递归分割（Recursive Partitioning）和回归树（Regression Tree）在R软件中应用的同时,对一前列腺癌数据使用生存分析和分类与回归树相结合的方法做出分析,并得到了对于疾病诊断和预防较有指导意义的结论。     

丘奇-图灵论点与人类认知能力和极限

丘奇-图灵论点是论述人类认知能力及其极限的一个重要背景。在此背景下,人类认知的无限性是 一种可数无限性,人的认知能力受递归规律的限制,并且只能在递归的意义上认知事物。对于非递归结构或非递 归性质的事物,人只能做递归性的认知。计算神经科学为计算主义认知观提供了一定的证据。     

On the convergence of some adaptive estimation procedures

In the paper we consider some adaptive procedures for estimating the unknown parameters of autoregressive and moving average processes. In case of AK(p) and MA(1) processes sequences of estimators converging with probability one and In mean square are given     

数据窥查效应、可靠性检验与平稳Bootstrap模拟——兼论技术交易规则与沪深300指数的预测

数据窥查效应是金融学研究中的一种常见现象,虽然很早就引起了学者们的关注,但由于研究方法的限制,目前国内还没有关于数据窥查效应的系统研究。为此,针对数据窥察效应,借助平稳Bootstrap模拟,探讨可靠性检验的统计学原理和计算步骤。构建了2 000多个模型,采用递归最小二乘的估计方法,研究是否可以利用技术交易规则预测沪深300指数的走势,并进一步探讨可靠性检验P值的动态演变过程,验证理论分析的结论,进而通过增加用于预测的样本长度来有效克服数据窥查效应。对平稳Bootstrap模拟区组选择的探讨表明,同时选择多个区组长度进行实证分析可以使结论更加稳健。     

The evaluation of trivariate normal probabilities defined by linear inequalities

This paper considers the evaluation of probabilities which are defined by a set of linear inequalities of a trivariate normal distribution. It is shown that these probabilities can be evaluated by a one-dimensional numerical integration. The trivariate normal distribution can have any covariance matrix and any mean vector, and the probability can be defined by any number of one-sided and two-sided linear inequalities. This affords a practical and efficient method for the calculation of these probabilities which is superior to basic simulation methods. An application of this method to the analysis of pairwise comparisons of four treatment effects is discussed.     

Detecting outliers: power and some other considerations

The general problem of outlier detection and five recursive outlier detection procedures considered in the study are defined. The methods to compute powers, probabilities of detecting ≥1 outliers, and >1 observations including at least one inlier as outliers are computed and results are discussed. Results show that no procedure is most powerful when the actual number of outlier present in the sample is exactly, under-, and overestimated. The probabilities of inliers being detected as outliers are also substantial particularly when outliers occur only on one side of the sample     

Nonparametric recursive estimation in stationary markov processes

This paper deals with a class of recursive kernel estimators of the transition probability density function t(y|x) of a stationary Markov process. A sufficient condition for such estimators to be weakly and strongly 2 consistent for almost all (x,y)∈R² is given. Further an L, convergence result is obtained. No continuity conditions are imposed on t(y|x).