Estimation of Dynamic Bivariate Mixture Models

This note compares a Bayesian Markov chain Monte Carlo approach implemented by Watanabe with a maximum likelihood ML approach based on an efficient importance sampling procedure to estimate dynamic bivariate mixture models. In these models, stock price volatility and trading volume are jointly directed by the unobservable number of price-relevant information arrivals, which is specified as a serially correlated random variable. It is shown that the efficient importance sampling technique is extremely accurate and that it produces results that differ significantly from those reported by Watanabe.     

Multilevel modelling of refusal and non-contact in household surveys: evidence from six UK Government surveys

Summary.  We analyse household unit non-response in six major UK Government surveys by using a multilevel multinomial modelling approach. The models are guided by current conceptual frameworks and theories of survey participation. One key feature of the analysis is the investigation of the extent to which effects of household characteristics are survey specific. The analysis is based on the 2001 UK Census Link Study, which is a unique source of data containing an unusually rich set of auxiliary variables. The study contains the response outcome of six surveys, linked to census data and interviewer observations for both respondents and non-respondents.     

A Note on Regression-Type Estimators Using Multiple Auxiliary Information

Kiregyera (1984), Mukerjee, Rao & Vijayan (1987), and Tripathi & Ahmed (1995) considered a number of regression-type estimators where information on two auxiliary variables related to study variable is available at different levels. Mukerjee et al . (1987) suggested three estimators and computed their mean square errors, but the computations seem to be incorrect. This note corrects them, and finds their estimators are no better than that of Kiregyera (1984). The estimator suggested by Tripathi & Ahmed (1995) is the best in the sense of having the smallest mean square error.     

Robust models in probability sampling

In the estimation of a population mean or total from a random sample, certain methods based on linear models are known to be automatically design consistent, regardless of how well the underlying model describes the population. A sufficient condition is identified for this type of robustness to model failure; the condition, which we call 'internal bias calibration', relates to the combination of a model and the method used to fit it. Included among the internally bias-calibrated models, in addition to the aforementioned linear models, are certain canonical link generalized linear models and nonparametric regressions constructed from them by a particular style of local likelihood fitting. Other models can often be made robust by using a suboptimal fitting method. Thus the class of model-based, but design consistent, analyses is enlarged to include more realistic models for certain types of survey variable such as binary indicators and counts. Particular applications discussed are the estimation of the size of a population subdomain, as arises in tax auditing for example, and the estimation of a bootstrap tail probability.     

粮食主产区农户稻作经营行为的调查与分析——基于江西省26县1058户农户调查数据

对江西省粮食主产区8个地级市26县32乡（镇）1058户农户进行问卷调查，内容涉及样本农户的稻作经营行为意愿及其影响因素的村域特征、农户户主及家庭特征、农户生产经营特征、对粮食补贴政策的认知及其它行为控制认知特征。在具体分析调查内容的基础上提出激励江西农户发展水稻生产的若干政策建议。     

以就业为导向的会计本科教育的培养目标研究——基于国内部分高校的问卷调查

文章从会计职业技能和全国14所高校的调查数据两方面对会计本科教育的培养目标进行分析,提出了以就业为导向的培养目标:培养具有良好的会计职业道德、具有扎实的经济管理理论基础、具备较强的会计专业技能、能适应社会经济发展需要、胜任会计工作的应用复合型人才.     

我国“第二代”农民工文化环境问题研究与对策——以重庆市为例

全国农民工这个流动群体已经发展到2亿之众,加强文化建设,提高全民族文明素质,加强农民工文化建设是城市文明创建的重要组成部分.通过对农民工文化环境的现状调研,发现问题从而提出提升进城务工农民精神文化生活质量的具体对策.     

Computational methods for complex stochastic systems: a review of some alternatives to MCMC

We consider analysis of complex stochastic models based upon partial information. MCMC and reversible jump MCMC are often the methods of choice for such problems, but in some situations they can be difficult to implement; and suffer from problems such as poor mixing, and the difficulty of diagnosing convergence. Here we review three alternatives to MCMC methods: importance sampling, the forward-backward algorithm, and sequential Monte Carlo (SMC). We discuss how to design good proposal densities for importance sampling, show some of the range of models for which the forward-backward algorithm can be applied, and show how resampling ideas from SMC can be used to improve the efficiency of the other two methods. We demonstrate these methods on a range of examples, including estimating the transition density of a diffusion and of a discrete-state continuous-time Markov chain; inferring structure in population genetics; and segmenting genetic divergence data.     

基于时间序列分析方法的连续性抽样调查研究

针对连续性抽样调查中如何利用过去各期的调查信息来提高现期抽样估计精度的问题，引入时间序列分析方法，分别考虑连续性抽样调查中重复样本和重叠样本等不同情况，建立了不同情况下的时间序列模型，利用成熟的时间序列分析方法给出了总体特征的线性组合估计量。由于时间序列分析方法能够充分利用以往各期的调查信息，从而能够给出精度更高的估计量。     

多次采样与希尔伯特变换

针对文献［１］提出的多次采样方法，证明了多次采样法在一定条件下等效于一种希尔伯特变换器，并用数据证明：当信号的相对带宽小于８０％时，９次采样所获得的正交分量误差可忽略。