Comparison of methods of estimation of parameters of the weibull distribution

The generalised least squares, maximum likelihood, Bain-Antle 1 and 2, and two mixed methods of estimating the parameters of the two-parameter Weibull distribution are compared. The comparison is made using (a) the observed relative efficiency of parameter estimates and (b) themean squared relative error in estimated quantiles, to summarize the results of 1000 simulated samples of sizes 10 and 25. The results are that: generalised least squares is the best method of estimating the shape parameter ß the best method of estimating the scale parameter a depends onthe size of ß for quantile estimation maximum likelihood is best Bain-Antle 2 is uniformly the worst of the methods.     

Table of percentage points of ratios of linear combinations of order statistics of samples from exponential distributions

A form of the distribution function of ratios of linear combinations of order statistics of samples from an exponential distribution is given. From the distribution, tables of percentage points of the statistic for α = .05, .95, and n = 3(1)50, and for censoring up to five observations are presented. Use of the tables is made to find critical values of the most powerful scale and location invariant test of exponentiality against uniformity, and also to find critical values for a test of outliers in an exponential population.     

Comparison of dispersions of mixtures of ordered distributions

In the paper we consider mixtures of unknown stochastically ordered distribution functions according to known mixing distribution functions. We provide optimal lower and upper bounds on ratios of general dispersion measures of such mixtures. The bounds do not depend on the particular form of dispersion measure. We present applications of the results in reliability theory, insurance mathematics, Bayesian statistics, and regression analysis.     

Minutes of Committee of Presidents of Statistical Societies

The general approach to generating random variates through transformations with multiple roots is discussed. Multinomial probabilities are determined for the selection of the different roots. An application of the general result yields a new and simple technique for the generation of variates from the inverse Gaussian distribution.     

Estimation of an indicator of the representativeness of survey response

Nonresponse is a major source of estimation error in sample surveys. The response rate is widely used to measure survey quality associated with nonresponse, but is inadequate as an indicator because of its limited relation with nonresponse bias. Schouten et al. (2009) proposed an alternative indicator, which they refer to as an indicator of representativeness or R-indicator. This indicator measures the variability of the probabilities of response for units in the population. This paper develops methods for the estimation of this R-indicator assuming that values of a set of auxiliary variables are observed for both respondents and nonrespondents. We propose bias adjustments to the point estimator proposed by Schouten et al. (2009) and demonstrate the effectiveness of this adjustment in a simulation study where it is shown that the method is valid, especially for smaller sample sizes. We also propose linearization variance estimators which avoid the need for computer-intensive replication methods and show good coverage in the simulation study even when models are not fully specified. The use of the proposed procedures is also illustrated in an application to two business surveys at Statistics Netherlands.     

Nonparametric estimation of the number of components of a superposition of renewal processes

Suppose all events occurring in an unknown number (ν)$(\nu )$ of iid renewal processes, with a common renewal distribution F  , are observed for a fixed time τ$\tau$, where both ν$\nu$ and F   are unknown. The individual processes are not known a priori, but for each event, the process that generated it is identified. For example, in software reliability application, the errors (or bugs) in a piece of software are not known a priori, but whenever the software fails, the error causing the failure is identified. We present a nonparametric method for estimating ν$\nu$ and investigate its properties. Our results show that the proposed estimator performs well in terms of bias and asymptotic normality, while the MLE of ν$\nu$ derived assuming that the common renewal distribution is exponential may be seriously biased if that assumption does not hold.     

A comparison of estimators of the degree of insect control

The current estimator of the degree of insect control by an insecticide in a field experiment laid out in randomized blocks is equal to one minus the cross-product ratio of a two way table of total insect counts over blocks. Since much work has been done on estimation of the common odds ratio of a number of strata in medical studies, a series of Monte Carlo studies was performed to investigate the possible use of these estimators and their standard errors in estimating the common degree of inject control of a number of blocks. Maximum likelihood, Mantel-Haenszel, and empirical logit estimators were evaluated and compared with back-transformed means over blocks, of cross-product ratios on the arithmetic, logarithmic, and arcsine scales. Maximum likelihood and Mantel-Haenszel estimators had the smallest mean squared errors, but their standard error estimates were only appropriate when sampling distributions were approximately Poisson and there was little heterogeneity among plots within blocks in the natural rates of population change.     

Efficient computation of the performance of bootstrap and jackknife estimators of the variance of L-statistics

Calculation of the bootstrap and the jackknife estimators of the variance of a statistic often relies on approximation techniques because the exact values are difficult if not impossible to obtain analytically. For the special case where the statistic is a linear combination of order statistics we propose to calculate the exact values combinatorically, thus completely eliminating the second-stage simulation error.     

Estimation of variance of mean using known coefficient of variation

An optimum unbiased estimator of the variance of mean is given It is defined as a function of the mean and itscustomary unbiased variance estimator, utilizing known coefficient of variation, skewness and kurtosis of the underlying distributions. Exact results are obtained. Normal and large sample cases receive particular treatment. The proposed variance estimator is generally more efficient than the customary variance estimator; its relative efficiency becomes appreciably higher for smaller coefficient of variation, smaller sample (in the normal case at least), higher negative skewness, or higher positive skewness with sufficiently large kurtosis. The empirical findings are reassuring and supportive.     

Simulation of extremes of diffusions

The authors consider the problem of simulating the times of events such as extremes and barrier crossings in diffusion processes. They develop a rejection sampler based on Shepp [Shepp, Journal of Applied Probability 1979; 16:423–427] for simulating an extreme of a Brownian motion and use it in a general recursive scheme for more complex simulations, including simultaneous simulation of the minimum and maximum and application to more general diffusions. They price exotic options that are difficult to price analytically: a knock‐out barrier option with a modified payoff function, a lookback option that includes discounting at the risk‐free interest rate, and a chooser option where the choice is made at the time of a barrier crossing. The Canadian Journal of Statistics 38: 738–755; 2010 © 2010 Statistical Society of Canada     

A comparison of estimators of the precision of two instruments

Three different estimators of the variance in errors of measurement, or the “imprecisions” of instruments, have been proposed (Hahn and Nelson, 1970; Maxwell, 1974; Hanumara: 1975) for the case where two measurements by one instrument and one measurement by a second instrument are available for each item. The estimators are compared by studying the probabilities of negative values, the biases, the variances, and the mean square errors. Based on these results, Hahn and Nelson's estimators for the variances in errors of measurement are recommended. Simultaneous confidence intervals of the imprecisions of instruments are also given.     

SNA关于机构部门分类的修订与中国机构部门的调整研究

 本文对2008SNA关于机构部门分类的修订进行了梳理。对比了2008SNA和1993SNA在机构部门与机构单位内容上的主要区别,分析了中国国民经济核算体系关于机构部门分类所存在的问题,提出了机构部门相关核算方法的调整建议。     

Automatic repeated-loess decomposition of data consisting of sums of oscillatory curves

Repeated loess is a nonparametric procedure that uses progressive smoothing and differencing to decompose data consisting of sums of curves. Smoothing is by locally weighted polynomial regression. Here the procedure was developed so that the decomposition into components was controlled automatically by the number of maxima in each component. The level of smoothing of each component was chosen to maximize the estimated probability of the observed number of maxima. No assumptions were made about the periodicity of components and only very weak assumptions about their shapes. The automatic procedure was applied to simulated data and to experimental data on human visual sensitivity to line orientation.An erratum to this article can be found at     

SAS软件的应用——基于ARMA模型的商品销售额的预测分析

文章运用SAS软件系统中的一些时间序列建模方法及回归分析方法对某商品的月销售额作了预测分析,得到了较高的预测精度,在实际应用中预测值的准确对于指导商家的战略决策起着重要作用.     

三分位数的意义及计算

统计中已有确定中位数、四分位数、十分位数等的方法,文章在此基础上提出三分位数的概念及其确定的方法。三分位数从另一个角度对总体的分布特征进行描绘,对于现象总体的内部构成,对于由不同组成部分、类型形成的总体的认识,对于分组时组间界限的确定,甚至对于分层抽样中层的划分,对于研究分析总体分布的离散程度都有一定的作用。同时对单项式设计的变量数列确定中位数、三分位数的方法也进行了一定的论述。     

Consistency of jackknife estimators of the variances of simple quantiles

Let σ² be the asymptotic variance of the sample p-quantile (0<p<1). Consistency of the delete-d jackknife estimators of σ² with d being a fraction of n is proved under very weak conditions. Some other results, such as the asymptotic orders of the moments of the jackknife histograms and an analog of the generalized Helly's theorem, are also established.     

A Comparison of Analysis of Covariate-Adjusted Residuals and Analysis of Covariance

Various methods to control the influence of a covariate on a response variable are compared. These methods are ANOVA with or without homogeneity of variances (HOV) of errors and Kruskal–Wallis (K–W) tests on (covariate-adjusted) residuals and analysis of covariance (ANCOVA). Covariate-adjusted residuals are obtained from the overall regression line fit to the entire data set ignoring the treatment levels or factors. It is demonstrated that the methods on covariate-adjusted residuals are only appropriate when the regression lines are parallel and covariate means are equal for all treatments. Empirical size and power performance of the methods are compared by extensive Monte Carlo simulations. We manipulated the conditions such as assumptions of normality and HOV, sample size, and clustering of the covariates. The parametric methods on residuals and ANCOVA exhibited similar size and power when error terms have symmetric distributions with variances having the same functional form for each treatment, and covariates have uniform distributions within the same interval for each treatment. In such cases, parametric tests have higher power compared to the K–W test on residuals. When error terms have asymmetric distributions or have variances that are heterogeneous with different functional forms for each treatment, the tests are liberal with K–W test having higher power than others. The methods on covariate-adjusted residuals are severely affected by the clustering of the covariates relative to the treatment factors when covariate means are very different for treatments. For data clusters, ANCOVA method exhibits the appropriate level. However, such a clustering might suggest dependence between the covariates and the treatment factors, so makes ANCOVA less reliable as well.     

Measures of lack of fit from tests of chi-squared type

If X² is the Pearson chi-squared statistic for testing fit, then $\text{X}{}^2\text{n}$ has long been considered an associated measure of the degree of lack of fit. Here we consider two classes of statistics of chi-squared type, each having X² as a member. The first is a class of directed divergence statistics discussed by Cressie and Read, the second consists of nonnegative definite quadratic forms in the standardized cell frequencies. We investigate the large sample behavior of $\text{T}\text{n}$, where T is any of these statistics. A number of auxiliary results on the Cressie-Read statistics are also obtained. The measures are illustrated by application to data from classical physics compiled by Stigler.     

Interpretation of the coefficient of determination of an ANOVA model as a measure of polarization

In this paper, it is demonstrated that coefficient of determination of an ANOVA linear model provides a measure of polarization. Taking as the starting point the link between polarization and dispersion, we reformulate the measure of polarization of Zhang and Kanbur using the decomposition of the variance instead of the decomposition of the Theil index. We show that the proposed measure is equivalent to the coefficient of determination of an ANOVA linear model that explains, for example, the income of the households as a function of any population characteristic such as education, gender, occupation, etc. This result provides an alternative way to analyse polarization by sub-populations characteristics and at the same time allows us to compare sub-populations via the estimated coefficients of the ANOVA model.