Authorized variance analysis with consumption rate

On the base of budget or plan information, comparing with actual results, making variance analysis, finding real reasons behind variance, this is important way of control and also important function of budget. However, without consideration of changes of environment, there are some limitations for static variance analysis with benchmark of plan data. Adjusting for benchmark according to actual condition, then doing variance analysis, these will improve utilization of variance analysis. Adjusted benchmark is often known as authorized cost/profit. For different understanding for authority concept, with RCA＇s （the abbreviation for Resource Consumption Accounting） view and numerical examples, this paper brings out the concept of consumption rate variance to promoting deeper understanding and analyzing reasons behind variance.     

The analysis of unbalanced linear models with variance components

Statistical inference for fixed effects, random effects and components of variance in an unbalanced linear model with variance components will be discussed. Variance components will be estimated by Restricted Maximum Likelihood. Iterative procedures for computing the estimates, such as Fisher scoring and the EM-algorithm, are described.     

Optimality of balanced designs for minimum norm quadratic unbiased estimation of variance components

This paper develops an approximate theory for the optimality of balanced designs under minimum norm quadratic unbiased estimation of variance components in one-way classified data.     

A two-stage test for one-way analysis of variance

A two-stage analogue of the Ftest for one-way analysis of variance is proposed. Critical values are given for selected cases and the method of obtaining critical values is described. Comparisons of the one-stage and two-stage tests are made in terms of the expected sample size required to achieve a given power.     

An alternative model for the analysis of variance with random effects

The standard model for the analysis of variance with random effects implies, for the case of two independent variables, that single effects must be tested not against the error, but against the interaction mean squares. This causes, in comparison with the fixed effects AV, a considerable loss of test power, particularly for the 2 × 2 table. An alternative modelling of the interaction effect is proposed which completely avoids the loss of power.     

碎纸片的拼接复原数学模型的构建

本文讨论在碎纸机以不同方式破碎纸片的情况下建立碎纸片的拼接复原模型,以解决碎片数量巨大时人工拼接的难题,本文建立了三个具有针对性的模型。模型一:方差分析法下的碎纸片拼接模型。在以纵切方式破碎纸片的情况下,提取碎纸片左右边缘的灰度列向量,利用碎纸片边缘处为单边同宽空白区域的特殊性对碎纸片进行定位,再利用方差分析法和欧式距离解决了纵切碎纸片的拼接复原问题。模型二:文字行间距一致性的碎纸片拼接模型。以纵横方式破碎纸片,利用同行文字行间距一致性的主要特性可解决横向碎纸片的拼接复原问题,简化了模型,将离散的像素灰度矩阵平均化处理,进而利用欧氏距离对碎纸片进行匹配,得到了碎纸片复原后的完整图片。模型三:二值化Otsu算法的碎纸片拼接复原模型。本文从双面纵横破碎纸片的问题出发,建立了纸片二值化Otsu法拼接模型,先对碎纸片分组预处理,为将复杂模型简单化,再利用全局阈值方法中典型的Otsu法求取碎纸片的最佳阈值,以该阈值对碎纸片中所含灰度值信息进行划分实现二值化处理,将边缘区域明显化,利用统计学方法求取拼接后的纸片间成功匹配的像素点占纸片边缘的概率,最终双面纵横破碎纸片的拼接复原问题得以解决。     

Variance estimation in the change analysis of a linear regression model

We study a “direct test” of Chu and White (1992) proposed for detecting changes in the trend of a linear regression model. The power of this test strongly depends on a suitable estimation of the variance of the error variables involved. We discuss various types of variance estimators and derive their asymptotic properties under the null-hypothesis of “no change” as well as under the alternative of “a change in linear trend”. A small simulation study illustrates the estimators' finite sample behaviour.     

Minimax estimators of a normal variance

In the estimation problem of unknown variance of a multivariate normal distribution, a new class of minimax estimators is obtained. It is noted that a sequence of estimators in our class converges to the Stein's truncated estimator. Received: March 1998     

Realized range-based estimation of integrated variance

We provide a set of probabilistic laws for estimating the quadratic variation of continuous semimartingales with the realized range-based variance—a statistic that replaces every squared return of the realized variance with a normalized squared range. If the entire sample path of the process is available, and under a set of weak conditions, our statistic is consistent and has a mixed Gaussian limit, whose precision is five times greater than that of the realized variance. In practice, of course, inference is drawn from discrete data and true ranges are unobserved, leading to downward bias. We solve this problem to get a consistent, mixed normal estimator, irrespective of non-trading effects. This estimator has varying degrees of efficiency over realized variance, depending on how many observations that are used to construct the high–low. The methodology is applied to TAQ data and compared with realized variance. Our findings suggest that the empirical path of quadratic variation is also estimated better with the realized range-based variance.     

Bayesian estimation of transition probabilities from repeated cross sections

This paper discusses some simple practical advantages of Markov chain Monte Carlo (MCMC) methods in estimating entry and exit transition probabilities from repeated independent surveys. Simulated data are used to illustrate the usefulness of MCMC methods when the likelihood function has multiple local maxima. Actual data on the evaluation of an HIV prevention intervention program among drug users are used to demonstrate the advantage of using prior information to enhance parameter identificaiton. The latter example also demonstrates an important strength of the MCMC approach, namely the ability to make inferences on arbitrary functions of model parameters.     

A test for the weights of the global minimum variance portfolio in an elliptical model

In this paper we consider the weights of the global minimum variance portfolio (GMVP). The returns are assumed to follow a matrix elliptically contoured distribution, i.e., the returns are assumed to be neither independent nor normally distributed. A test for the general linear hypothesis is given. The distribution of the test statistic is derived under the null and the alternative hypothesis. It turns out that its distribution is invariant with respect to the type of the matrix elliptical distribution, i.e., the stochastic properties of the GMVP do not depend either on the mean vector or on the distributional assumptions imposed on asset returns. In an empirical study we analyze an international diversified portfolio.     

A theory of robust long-run variance estimation

Long-run variance estimation can typically be viewed as the problem of estimating the scale of a limiting continuous time Gaussian process on the unit interval. A natural benchmark model is given by a sample that consists of equally spaced observations of this limiting process. The paper analyzes the asymptotic robustness of long-run variance estimators to contaminations of this benchmark model. It is shown that any equivariant long-run variance estimator that is consistent in the benchmark model is highly fragile: there always exists a sequence of contaminated models with the same limiting behavior as the benchmark model for which the estimator converges in probability to an arbitrary positive value. A class of robust inconsistent long-run variance estimators is derived that optimally trades off asymptotic variance in the benchmark model against the largest asymptotic bias in a specific set of contaminated models.     

Estimation of finite population variance using random non-response in survey sampling

In this paper, an estimator of finite population variance proposed by Isaki (1983) is studied under the two different situations of random non-response suggested by Tracy and Osahan (1994). A distribution is proposed for the number of sampling units on which information could not be obtained due to random non-response. The estimators for the mean square errors of the proposed strategies are also suggested. This paper was written while both authors were members of the Dept. of Econometrics, Monash University, Clayton 3168, Australia. This paper was presented on SISC—1996, Sydney, Australia. The opinions and results discussed in this paper are of authors and not necessarily of their institutes.     

Locally minimum variance unbiased estimator in a discontinuous density function

The exact forms of the locally minimum variance unbiased estimators and their variances are given in the case of a discontinuous density function.     

Bayesian loss given default estimation for European sovereign bonds

We develop and apply a Bayesian model for the loss rates given defaults (LGDs) of European Sovereigns. Financial institutions are in need of LGD forecasts under Pillar II of the regulatory Basel Accord and the downturn in LGD forecasts under Pillar I. Both are challenging for portfolios with a small number of observations such as sovereigns. Our approach comprises parameter risk and generates LGD forecasts under both regular and downturn conditions. With sovereign-specific rating information, we found that average LGD estimates vary between 0.46 and 0.64, while downturn estimates lay between 0.50 and 0.86.     

Estimation of error variance in one-way random model

A preliminary-test estimator for the error variance in the one-way random model is considered. The optimum levels of significance for the preliminary test are obtained based on a regret function. A pooling procedure for estimating the error variance, based on weighting functions, is also considered. A comparison of these estimators is made.     

Bayesian point estimation of the cointegration space

A neglected aspect of the otherwise fairly well developed Bayesian analysis of cointegration is point estimation of the cointegration space. It is pointed out here that, due to the well known non-identification of the cointegration vectors, the parameter space is not Euclidean and the loss functions underlying the conventional Bayes estimators are therefore questionable. We present a Bayes estimator of the cointegration space which takes the curved geometry of the parameter space into account. This estimate has the interpretation of being the posterior mean cointegration space and is invariant to the order of the time series, a property not shared with many of the Bayes estimators in the cointegration literature. An overall measure of cointegration space uncertainty is also proposed. Australian interest rate data are used for illustration. A small simulation study shows that the new Bayes estimator compares favorably to the maximum likelihood estimator.     

Bayesian estimation of inefficiency heterogeneity in stochastic frontier models

Estimation of the one sided error component in stochastic frontier models may erroneously attribute firm characteristics to inefficiency if heterogeneity is unaccounted for. However, unobserved inefficiency heterogeneity has been little explored. In this work, we propose to capture it through a random parameter which may affect the location, scale, or both parameters of a truncated normal inefficiency distribution using a Bayesian approach. Our findings using two real data sets, suggest that the inclusion of a random parameter in the inefficiency distribution is able to capture latent heterogeneity and can be used to validate the suitability of observed covariates to distinguish heterogeneity from inefficiency. Relevant effects are also found on separating and shrinking individual posterior efficiency distributions when heterogeneity affects the location and scale parameters of the one-sided error distribution, and consequently affecting the estimated mean efficiency scores and rankings. In particular, including heterogeneity simultaneously in both parameters of the inefficiency distribution in models that satisfy the scaling property leads to a decrease in the uncertainty around the mean scores and less overlapping of the posterior efficiency distributions, which provides both more reliable efficiency scores and rankings.     

Efficiencies for optimum designs when transforming the response in nonlinear models with nonconstant variance

Often the responses from mechanistic models have to be transformed to achieve error distributions that are symmetric and have constant variance. Because of the nature of the relationship between the response and the mechanistic model, it is necessary to transform both sides of the model. Expressions are given for the parameter sensitivities in the transformed model and examples given of optimum designs for particular values of λ, together with the efficiency of these designs as λ varies. Approaches to finding designs robust to variations in λ are indicated and exemplified.     

A comparison of the ratio of variances in distance-based and classical multivariate analysis

In multivariate analysis, the measure of variance accounted for plays a central role. In this paper, we show that an alternative approach, distance-based multivariate analysis, also yields solutions that can be summarized by a ratio of variances. For classical multivariate analysis, this ratio is equal to the variance accounted for (VAF) and in distance-based multivariate analysis it equals distance accounted for (DAF). We show that DAF in distance-based multivariate analysis can always be made higher than VAF in classical multivariate analysis. This property is illustrated for principal components analysis, multiple correspondence analysis, multiple regression, and analysis of variance.