最小平方距离法和隐式线性函数关系的参数估计

本文再次指出:最小平方距离法(LSD),也可叫最小模方法,是从解决多维空间n个点的超平面拟合问题而提取的;通过对p个随机变量的n组观测值,此方法是探求它们之间是否存在隐式线形函数关系的好方法;使用此法,可以得出隐式线形函数关系较好的参数估计。     

关于扩散方程漂移系数估计方法的改进

本文提出了一种新的基于扩散过程轨道构造漂移系数样本的方法—对数增量法,通过理论及模拟分析说明了在适当条件下,特别是对于大多数金融数据,基于对数增量法获得的漂移系数估计量的收敛速度及有限样本性质均比基于传统的"直接增量法"所得到的结果要好。     

OPERATIONAL MODAL ANALYSIS SCHEMES USING CORRELATION TECHNIQUE

For some large-scale engineering structures in operating conditions, modal parameters estimation must base itself on response-only data. This problem has received a considerable amount of attention in the past few years. It is well known that the cross-correlation function between the measured responses is a sum of complex exponential functions of the same form as the impulse response function of the original system. So this paper presents a time-domain operating modal identification global scheme and a frequency-domain scheme from output-only by coupling the cross-correlation function with conventional modal parameter estimation. The outlined techniques are applied to an airplane model to estimate modal parameters from response-only data.     

指令性抽样下总体均值和方差的估计及其应用

指令性抽样下的样本往往不具有代表性,因此仅用它们来推断总体将是不适合的,这篇文章基于观察信息,利用概率统计方法将没有发出调查指令的样本信息补充出来,然后利用观察信息和补充信息一起来对总体进行推断.具体我们给出了总体均值和方差参数估计的迭代公式,并给出它们在经济犯罪调查和流行病调查中的应用.     

二项分布的参数估计问题研究

本文主要讨论二项分布的参数估计问题.对GB/T 4087.1-1983,GB/T 4087.2-1983,GB/T4087.3-1985给出的经典二项分布参数点估计、区间估计和可靠度置信下限计算方法进行了分析,指出了其中存在的问题.根据二项分布的数学表达式推导出了二项分布参数的概率分布密度函数,在此基础上提出了进行二项分布参数估计的一般方法.     

Richards模型参数估计及其模型应用

在非线性模型中,Richards模型是一个含有四参数的增长曲线模型,该模型对数据的拟合有较强的适应性,应用较为广泛.但其参数的估计较为复杂,给出简便易行的三种方法,实例应用表明拟合效果很好.     

基于Jackknife模型平均的社会用电量预测研究

针对社会用电量波动的复杂性,文章将Jackknife模型平均理论引入社会用电量分析与预测研究.中,通过加权平均不同模型的预测值,最大限度减少有用信息的遗失,以提高社会用电量预测准确度.通过选取中国和美国不同时期社会用电量数据集,并使用各类预测误差指标以及Diebold-Mariano检验法,来验证所提出的Jackkni...     

基于局部多项式展开的多元非参数模型贝叶斯带宽选择

在多元非参数模型中带宽和阶的选择对局部多项式估计量的表现十分重要。本文基于交叉验证准则提出一个自适应贝叶斯带宽选择方法。在给定的误差密度函数下,该方法可推导出对应的似然函数,并构造带宽参数的后验密度函数。随后,通过带宽的后验期望可同时获得阶和带宽的估计。数值模拟的结果表明,该方法不仅比大拇指准则方法精确,且比交叉验证方法耗时更少。与此同时,与Nadaraya-Watson估计相比,所提带宽选择方法对多元非参数模型的适应性要更好。最后,本文通过一组实际数据说明有限样本下所提贝叶斯带宽选择的表现很好。     

Speech wideband extension based on Gaussian mixture model

To decrease the spectral distortion of highband envelope, the function of spectral distortion and mutual information between feature vector and highband envelope was studied, and an extended Gaussian Mixture Model （GMM） bandwidth extension algorithm was proposed based on the research. The feature parameters which have larger mutual information with highband envelope were selected to constitute the feature vector, and the GMM was adopted to compute the joint probability density of the feature vector and highband envelope. Then the highband envelope was estimated via the posterior probabilities computed from the model parameters estimated by Expectation-Maximization （EM） algorithm. The experimental results show that the spectral distortion is lower than the algorithm, such as the traditional algorithm based on GMM, by 0.3 dB and the number of frames with spectral distortion over 10 dB sharply reduced over 50%.