关于几种统计平均数关系式的证明及推广

统计平均数是统计定量分析的重要方法,在统计学中有着及其重要的地位。统计平均数按代表意义和计算方式的不同,可以分为数值平均数和位置平均数,其中常用的数值平均数有算术平均数(X)、调平均数(H)和几何平均数(G),三者源于同一个家族,并存在如下关系式:H相似文献   

调和平均数的两种计算功能

平均数有算术平均数、调和平均数、几何平均数、中位数和众数五种，其中除了中位数和众数是位置平均数外，根据数值计算的平均数有算术平均数、调和平均数、几何平均数，也称为数值平均数。在三种数值平均数中，算术平均数和几何平均数是独立的平均数，它们有各自的适用范围。而对于调和平均数，有些人认为调和平均数不过是算术平均数的一种变形，不是独立的平均数。这种看法实际是不全面的。我在几年的统计学教学过程中，总结出调和平均数有两方面的计算功能，即在不同的资料条件下，一、调和平均数是算术平均数的一种变形；二、调和平均数…     

对三大数值平均数之关系成立的条件补充

大多数《统计学原理》教科书把平均数分成两大类 :一类是数值平均数 ,包括算术平均数、调和平均数和几何平均数 ;另一类是位置平均数 ,包括众数、中位数等。而且都认为 ,对同一数列计算的三大数值平均数之间存在这样的数量关系 ,即几何平均数大于调和平均数而小于算术平均数 ,只有当所有的变量值相同时 ,三大平均数才相等。用数学公式表示 ,它们之间的关系式为 : Ｘ≥Ｇ≥Ｈ笔者以为 ,上述三大数值平均数之关系成立的条件是 :数列中的各个变量值都为正数 ,不能为负数和零。现举例说明 ,在数列中 ,若有负数和零 ,上述关系不能成立。例一 ,甲…     

算术平均数与调和平均数的关系探究

文章认为,算术平均数和调和平均数是平均指标的两种表现形式。算术平均数和调和平均数并非两类独立的平均数;算术平均数和调和平均数的数值之间并无直接关系,也不存在谁大谁小的问题;不能根据同一资料既计算算术平均数,又计算调和平均数,否则就是纯数字游戏,而非统计研究。     

标准差何以标准？

平均数是统计工作中最常用的指标之一。它反映事物分布的集中趋势 ,是总体的一种代表值 ,代表某种事物在一定时间地点条件下所达到的一般水平。然而 ,平均数又具有抽象性 ,将总体内部水平和分布的差异抽象了 ,两个数值相等的平均数 ,可能掩盖着两个总体内部差异相差甚大的情况 ,使两个水平相等的平均数之代表性却高低悬殊。为了说明平均数代表性的高低 ,或反映不同变量数列离散度的大小 ,人们通常都会提到变异全距 ( R)、平均差 ( A· D)、标准差( σ)和离散系数 ( V)。其中 ,R是变量的两个极端值之差 (故又称极差 ) ,差异大小完全取决于…     

关于平均数和数学期望的应用

在统计学中常用平均数来表述数据(或数列)集中趋势的测度.平均数有:算术平均数、调和平均数、几何平均数、中位数及众数.各种平均数有各自的定义和它们应用的范围.算术平均数是最常用的一种.它有简单算术平均数与加权算术平均数之分.简单算术平均数是将总体的各个单位标志值简单相加,然后除以单位个数,求出平均标志值;加权算术平均数是用各组标志值乘以相应的总体单位数来计算的,由此,它的大小不仅取决于总体各单位的变量值,而且受单位变量重复出现的次数影响,各组出现的次数在这里面起了权衡轻重的作用.调和平均数是各个变量值倒数的算术平均数,又称倒数平均数.在统计工作中应用机会很少,往往是作为算术平均数的变形来使用的.     

统计平均数的统计学机理研究与应用

统计平均数是社会经济统计分析中应用最广泛、最重要的综合指标之一,在统计理论研究和其他领域中均有极为广泛的应用。文章主要针对统计平均数进行了系统的研究,从统计平均数的各个种类上做了详细的阐述,并揭示了各平均数之间的内在联系。同时,重点对统计平均数的数学机理及应用误区做了研究,并对如何正确地应用统计平均数提出了一些可借鉴的原则。     

几何平均数不是静态平均数

几何平均数全部是动态平均数?还是有的是静态平均数,有的是动态平均数?这个看似极其简单的问题,却关系重大。它涉及到几何平均数统计理论的教与学,也涉及到几何平均数在统计实际工作中的运用。因此,不能不弄清楚。     

使用平均数应注意的几个问题

在现象的发展过程中,必然性的东西总是和特殊的偶然性的因素交织在一起.统计分析的主要目的之一就是为了揭示现象一般典型的特征,即必然性和规律性.要达到这个目的,就必需消除或减弱偶然因素变动的影响.而在平均数中,偶然变动的因素影响相互抵消,从而充分反映出某种必然性.因此,平均数比个别数值更能充分反映同类现象的本质.可以说,平均分析是统计分析的重要内容,而平均数则是平均分析的灵魂.平均数无论是在统计理论分析还是其它领域都有极为广泛的应用和十分重要的意义.     

集中量数的一些注记

集中量数是代表一组数据典型水平或集中趋势的统计量。常用的集中量数包括算术平均数、加权平均数、几何平均数、调和平均数、平方平均数、百分位数、四分位数、中位数、众数等。一、算术平均数可以很好地反映一组数据的集中趋势一组数据Xi,i=1,…,n的算术平均数n X=!1n"X(1)i     

Locally optimal test for simultaneously testing the mean and variance of a normal distribution

A generalization of the locally most powerful unbiased (LMPU) test for the single parameter case to the k-parameter case was proposed by SenGupta and Vermeire (1986). In particular we defined a locally most mean power unbiased (LMMPU) test based on the mean curvature of the power hypersurface. Compared to the type C tests of Neyman and Pearson and the type D tests (Isaacson, 1951), LMMPU tests possess better theoretical properties and enjoy ease of construction of critical regions. In this paper we present an interesting example of a two-parameter univariate normal population for which Isaacson (1951, p. 233) was unsuccessful in finding a type D test. For the case of one observation, we prove that no Type D region exists but the LMMPU test is obtained - it is an example of a test with singular Hessian matrix for its power but is nevertheless a strictly locally unbiased (LU) test.     

Gompertz-power series distributions

ABSTRACT

In this article, we introduce the Gompertz power series (GPS) class of distributions which is obtained by compounding Gompertz and power series distributions. This distribution contains several lifetime models such as Gompertz-geometric (GG), Gompertz-Poisson (GP), Gompertz-binomial (GB), and Gompertz-logarithmic (GL) distributions as special cases. Sub-models of the GPS distribution are studied in details. The hazard rate function of the GPS distribution can be increasing, decreasing, and bathtub-shaped. We obtain several properties of the GPS distribution such as its probability density function, and failure rate function, Shannon entropy, mean residual life function, quantiles, and moments. The maximum likelihood estimation procedure via a EM-algorithm is presented, and simulation studies are performed for evaluation of this estimation for complete data, and the MLE of parameters for censored data. At the end, a real example is given.     

An Adjustment to the Bartlett's Test for Small Sample Size

The Bartlett's test (1937) for equality of variances is based on the χ² distribution approximation. This approximation deteriorates either when the sample size is small (particularly < 4) or when the population number is large. According to a simulation investigation, we find a similar varying trend for the mean differences between empirical distributions of Bartlett's statistics and their χ² approximations. By using the mean differences to represent the distribution departures, a simple adjustment approach on the Bartlett's statistic is proposed on the basis of equal mean principle. The performance before and after adjustment is extensively investigated under equal and unequal sample sizes, with number of populations varying from 3 to 100. Compared with the traditional Bartlett's statistic, the adjusted statistic is distributed more closely to χ² distribution, for homogeneity samples from normal populations. The type I error is well controlled and the power is a little higher after adjustment. In conclusion, the adjustment has good control on the type I error and higher power, and thus is recommended for small samples and large population number when underlying distribution is normal.     

Power and Sample Size Determination When the Alternative Hypotheses are Given in Quantiles

The power of normal-theory tests about means depends on a noncentrality parameter which is a function of the unknown parameter σ. In order to calculate power and to solve sample-size problems based on power, differences between hypothesized and alternative values of the means are frequently selected as a multiple of σ, a choice which eliminates σ from the noncentrality parameter and permits a solution. Perhaps a more natural (but equivalent) way to express alternatives is to give one or more means as the quantile of order p (say Q_p ) of a distribution with another mean. As we will demonstrate, this kind of alternative also eliminates σ from the problem.     

Measurement error effect on the CUSUM control chart

The performance of the cumulative sum (CUSUM) control chart for the mean when measurement error exists is investigated. It is shown that the CUSUM chart is greatly affected by the measurement error. A similar result holds for the case of the CUSUM chart for the mean with linearly increasing variance. In this paper, we consider multiple measurements to reduce the effect of measurement error on the charts performance. Finally, a comparison of the CUSUM and EWMA charts is presented and certain recommendations are given.     

Tables for pairwise multiple comparisons using shaffer's modifiedsequentially-rejective procedure

A sequentially rejective (SR) testing procedure introduced by Holm (1979) and modified (MSR) by Shaffer (1986) is considered for testing all pairwise mean comparisons.For such comparisons, both the SR and MSR methods require that the observed test statistics be ordered and compared, each in turn, to appropriate percentiles on Student's t distribution.For the MSR method these percentiles are based on the maximum number of true null hypotheses remaining at each stage of the sequential procedure, given prior significance at previous stages, A function is developed for determining this number from the number of means being tested and the stage of the test.For a test of all pairwise comparisons, the logical implications which follow the rejection of a null hypothesis renders the MSR procedure uniformly more powerful than the SR procedure.Tables of percentiles for comparing K means, 3 < K < 6, using the MSR method are presented.These tables use Sidak's (1967) multiplicative inequality and simplify the use of t he MSR procedure.Several modifications to the MSR are suggested as a means of further increasing the power for testing the pairwise comparisons.General use of the MSR and the corresponding function for testing other parameters besides the mean is discussed.     

The Minimum Density Power Divergence Estimation for the Lognormal Density

In this article, we implement the minimum density power divergence estimation for estimating the parameters of the lognormal density. We compare the minimum density power divergence estimator (MDPDE) and the maximum likelihood estimator (MLE) in terms of robustness and asymptotic distribution. The simulations and an example indicate that the MDPDE is less biased than MLE and is as good as MLE in terms of the mean square error under various distributional situations.     

递归均值调整单位根检验能提高检验功效吗?

根据备择假设成立时均值是否为零,讨论了三类单位根检验统计量的检验功效。理论研究表明:在大样本下,检验功效只与检验类型有关,与均值取值无关。蒙特卡洛模拟表明:在有限样本下,均值会影响检验功效,当均值为零时,第一类DF检验功效最高;当均值大于零时,在绝大多数场合下,第二类DF检验功效最优。因此,递归均值调整单位根检验功效仅在有限条件下最大。     

Bayesian estimation of reliability using time-to-failure distribution of parametric degradation models

This paper focusses on computing the Bayesian reliability of components whose performance characteristics (degradation – fatigue and cracks) are observed during a specified period of time. Depending upon the nature of degradation data collected, we fit a monotone increasing or decreasing function for the data. Since the components are supposed to have different lifetimes, the rate of degradation is assumed to be a random variable. At a critical level of degradation, the time to failure distribution is obtained. The exponential and power degradation models are studied and exponential density function is assumed for the random variable representing the rate of degradation. The maximum likelihood estimator and Bayesian estimator of the parameter of exponential density function, predictive distribution, hierarchical Bayes approach and robustness of the posterior mean are presented. The Gibbs sampling algorithm is used to obtain the Bayesian estimates of the parameter. Illustrations are provided for the train wheel degradation data.     

A family of test statistics for trend change in mean residual life with unknown turning point using censored data

In this paper, we develop a family of test statistics for testing exponentiality against increasing then decreasing mean residual life alternatives to accommodate the randomly censored data when neither the change point nor the proportion is known. We establish the asymptotic null distribution of test statistics. Monte Carlo simulations are conducted to investigate the speed of convergence of test statistics to the asymptotic null distribution and study the performance of test statistics by the power of tests.