The Holodomor law and national trauma construction in Ukraine

This article proposes to conceptualize the remembrance of the 1932–33 famine, known as the Holodomor, as cultural trauma construction in Ukraine. This entails the study of how the memory of this devastating historical event became the national collective symbol of suffering with which Ukrainians identify today. Based on Jeffrey Alexander’s concept of cultural trauma, the analysis focuses on the role of political elites and their claim-making regarding the meaning of the famine. Focusing specifically on the 2006 Holodomor law as the main claim of the Ukrainian policy-makers, the article investigates their definition of the historical event, their naming of victims and perpetrators, and their social mediation of famine representations. The article reveals how, through their definition of the Holodomor as genocide, the political elites promoted the understanding that Ukrainians experienced the years of 1932–33 differently from other Soviet nations. The Holodomor law should therefore be seen in the context of Ukrainian nation-building policy, which aims to forge a distinct Ukrainian collective identity.     

个人教育投资的风险收益测度

将教育资本分解成受教育程度和职业的各种组合,借鉴Markowiz的有效投资组合理论,计算Mincer收入方程中扰动项的均值—标准差来衡量投资风险收益,以此建立中国教育投资风险和收益之间的平衡关系,并且利用F检验衡量投资组合的均值方差张成。结果表明,有效的教育资本组合不仅与所接受教育程度有关,还与未来职业选择有关。在以受教育程度为高中和中等职业的基础上,高中(06)可以张成其他全部14个投资组合的均值方差边界。因此,个体和政府在进行教育投资时,都需要全面衡量投资组合的风险收益平衡问题。     

Non-tariff barrier on chicken imports into Russia: Impact on production,trade and prices

We analyze the Russian restriction on chicken imports as an example of a non-tariff barrier (NTB). Cointegration test results support the hypothesis that the NTB limited trade. We calibrate an equilibrium market model to cointegration results to assess the market impact. The tariff equivalent of the Russian chicken NTB is estimated to be 30 − 40% depending on the representation of consumer demand. Removing the NTB decreases domestic production by 4–5% and domestic price by 27%–34%, while imports increase by 326–423 thousand tons annually in 2015–2019.     

Inference of non-centrality parameter of a truncated non-central chi-squared distribution

Non-central chi-squared distribution plays a vital role in statistical testing procedures. Estimation of the non-centrality parameter provides valuable information for the power calculation of the associated test. We are interested in the statistical inference property of the non-centrality parameter estimate based on one observation (usually a summary statistic) from a truncated chi-squared distribution. This work is motivated by the application of the flexible two-stage design in case–control studies, where the sample size needed for the second stage of a two-stage study can be determined adaptively by the results of the first stage. We first study the moment estimate for the truncated distribution and prove its existence, uniqueness, and inadmissibility and convergence properties. We then define a new class of estimates that includes the moment estimate as a special case. Among this class of estimates, we recommend to use one member that outperforms the moment estimate in a wide range of scenarios. We also present two methods for constructing confidence intervals. Simulation studies are conducted to evaluate the performance of the proposed point and interval estimates.     

Semiparametric inference of proportional odds model based on randomly truncated data

This paper studies the estimation in the proportional odds model based on randomly truncated data. The proposed estimators for the regression coefficients include a class of minimum distance estimators defined through weighted empirical odds function. We have investigated the asymptotic properties like the consistency and the limiting distribution of the proposed estimators under mild conditions. The finite sample properties were investigated through simulation study making comparison of some of the estimators in the class. We conclude with an illustration of our proposed method to a well-known AIDS data.     

Estimation in capture–recapture models when covariates are subject to measurement errors and missing data

For capture–recapture models when covariates are subject to measurement errors and missing data, a set of estimating equations is constructed to estimate population size and relevant parameters. These estimating equations can be solved by an algorithm similar to the EM algorithm. The proposed method is also applicable to the situation when covariates with no measurement errors have missing data. Simulation studies are used to assess the performance of the proposed estimator. The estimator is also applied to a capture–recapture experiment on the bird species Prinia flaviventris in Hong Kong. The Canadian Journal of Statistics 37: 645–658; 2009 © 2009 Statistical Society of Canada     

Characteristic function estimation of non-Gaussian Ornstein–Uhlenbeck processes

Continuous non-Gaussian stationary processes of the OU-type are becoming increasingly popular given their flexibility in modelling stylized features of financial series such as asymmetry, heavy tails and jumps. The use of non-Gaussian marginal distributions makes likelihood analysis of these processes unfeasible for virtually all cases of interest. This paper exploits the self-decomposability of the marginal laws of OU processes to provide explicit expressions of the characteristic function which can be applied to several models as well as to develop efficient estimation techniques based on the empirical characteristic function. Extensions to OU-based stochastic volatility models are provided.     

Semiparametric estimation for count data through weighted distributions

This paper is concerned with semiparametric discrete kernel estimators when the unknown count distribution can be considered to have a general weighted Poisson form. The estimator is constructed by multiplying the Poisson estimate with a nonparametric discrete kernel-type estimate of the Poisson weight function. Comparisons are then carried out with the ordinary discrete kernel probability mass function estimators. The Poisson weight function is thus a local multiplicative correction factor, and is considered as the uniform measure to detect departures from the equidispersed Poisson distribution. In this way, the effects of dispersion and zero-proportion with respect to the standard Poisson distribution are also minimized. This method of estimation is also applied to the weighted binomial form for the count distribution having a finite support. The proposed estimators, in addition to being simple, easy-to-implement and effective, also outperform the competing nonparametric and parametric estimators in finite-sample situations. Two examples illustrate this new semiparametric estimation.