湖南区域政策的演变——评价与反思

区域政策是影响区域经济的重要变量,文章对湖南的区域规划进行了分析,回顾了改革开放后湖南区域政策的演变,并采用系统熵、基尼系数、偏态系数等指标对其效果进行了评价。经过评价,发现湖南落后地区同省内发达地区的差距在不断扩大,但2010年后这一趋势有所放缓。针对以上问题,提出湖南区域经济发展的对策。     

The alpha–beta skew normal distribution: properties and applications

This paper introduces a new class of skew distributions by extending the alpha skew normal distribution proposed by Elal-Olivero [Elal-Olivero, D. Alpha-skew-normal distribution. Proyecciones. 2010;29:224–240]. Statistical properties of the new family are studied in details. In particular, explicit expressions for the moments and the shape parameters including the skewness and the kurtosis coefficients and the moment generating function are derived. The problem of estimating parameters on the basis of a random sample coming from the new class of distribution is considered. To examine the performance of the obtained estimators, a Monte Carlo simulation study is conducted. Flexibility and usefulness of the proposed family of distributions are illustrated by analysing three real data sets.     

关于常微分方程求解公式的注记

要对常微分方程中的一阶线性齐次方程以及一阶线性非齐次方程的求解公式进行一下更为明确的证明.     

A plant-capture approach for achieving complete coverage of a population

Sightings of, any member of a target population of unknown size occur according to a homogeneous Poisson process. The Poisson processes are independent and have a common rate. Individuals are tagged when first sighted. The population is augmented by the insertion of known number of planted individuals who have already been tagged and whose behaviour is identical to that of the other members. Consideration is given to two stopping rules for determining, within a specified error probability, when all members of the target population have been seen. A comparison is made of the average time to termination under these two rules. It is shown that, when the target population is small, the use of plants can provide a useful reduction in the average time taken to achieve complete coverage within the specified error probability.     

The performance of the linear and quadratic discriminant functions for three types of non-normal distribution

The purpose of thls paper is to investlgate the performance of the LDF (linear discrlmlnant functlon) and QDF (quadratic dlscrminant functlon) for classlfylng observations from the three types of univariate and multivariate non-normal dlstrlbutlons on the basls of the mlsclasslficatlon rate. The theoretical and the empirical results are described for unlvariate distributions, and the empirical results are presented for multivariate distributions. It 1s also shown that the sign of the skewness of each population and the kurtosis have essential effects on the performance of the two discriminant functions. The variations of the populatlon speclflc mlsclasslflcatlon rates are greatly depend on the sample slze. For the large dlmenslonal populatlon dlstributlons, if the sample sizes are sufflclent, the QDF performs better than the LDF. We show the crlterla of a cholce between the two discriminant functions as an application.     

Model Uncertainty and Model Averaging in the Estimation of Infectious Doses for Microbial Pathogens

Food‐borne infection is caused by intake of foods or beverages contaminated with microbial pathogens. Dose‐response modeling is used to estimate exposure levels of pathogens associated with specific risks of infection or illness. When a single dose‐response model is used and confidence limits on infectious doses are calculated, only data uncertainty is captured. We propose a method to estimate the lower confidence limit on an infectious dose by including model uncertainty and separating it from data uncertainty. The infectious dose is estimated by a weighted average of effective dose estimates from a set of dose‐response models via a Kullback information criterion. The confidence interval for the infectious dose is constructed by the delta method, where data uncertainty is addressed by a bootstrap method. To evaluate the actual coverage probabilities of the lower confidence limit, a Monte Carlo simulation study is conducted under sublinear, linear, and superlinear dose‐response shapes that can be commonly found in real data sets. Our model‐averaging method achieves coverage close to nominal in almost all cases, thus providing a useful and efficient tool for accurate calculation of lower confidence limits on infectious doses.     

On the robustness and johnson's modification of the one sample T-Statistic

Cicchitelli (1989) conducted an extensive Monte Carlo study to investigate the robustness of the one sample T-statistic under non-normal parent populations. He considered a rich family of distributions, viz., the generalized λ-distribution which was introduced by Ramberg et al. (1979), as the family of parent populations. We shall address and reinforce his empirical findings by means of Edgeworth expansion of the T-statistic. As the skewness of the parent population affects the T-statistic more than the kurtosis, Johnson (1978) suggested a modification to the T-statistic to reduce the effect of skewness. We investigate the performance of this modified T-statistic under the same family of distributions as Cicchitelli considered by means of a Monte Carlo study and give some recommendations on its use.     

Distributional properties of simpson's index of diversity

Closed expressions for the first four moments of Simpson's index of diversity are derived using techniaues suggested by Haldane (1937). As the samole size increases the behavior of the skewness and kurtosis is studied for several Dopulations with varying degrees of diversity, If the populationproportions decrease accordinq to a geometric progression, graphs of β₁and β₂ indicate that convergence to normality in general is more rapid for populations which are less diverse.     

Estimating the Period of a Cyclic Non‐Homogeneous Poisson Process

Abstract. Motivated by applications of Poisson processes for modelling periodic time‐varying phenomena, we study a semi‐parametric estimator of the period of cyclic intensity function of a non‐homogeneous Poisson process. There are no parametric assumptions on the intensity function which is treated as an infinite dimensional nuisance parameter. We propose a new family of estimators for the period of the intensity function, address the identifiability and consistency issues and present simulations which demonstrate good performance of the proposed estimation procedure in practice. We compare our method to competing methods on synthetic data and apply it to a real data set from a call center.     

Bayesian analysis of the linear regression model with an edgeworth series prior distribution

The purpose of the present investigation 1s to observe the effect of departure from normahty of the prior distribution of regresslon parameters on the Bayman analysis of a h e a r regresslon model Assuming an Edgeworth serles prior distribution for the regresslon coefficients and gamma prior for the disturbances precision, the expressions for the posterlor distribution, posterlor mean and Bayes risk under a quadratic loss function are obtalned The results of a numerical evaluation are also analyzed