Inference for Random Coefficient INAR(1) Process Based on Frequency Domain Analysis

In this article, we present the explicit expressions for the higher-order moments and cumulants of the first-order random coefficient integer-valued autoregressive (RCINAR(1)) process. The spectral and bispectral density functions are also obtained, which can characterize the RCINAR(1) process in the frequency domain. We use a frequency domain approach which is named Whittle criterion to estimate the parameters of the process. We propose a test statistic which is based on the frequency domain approach for the hypothesis test, H₀: α = 0?H₁: 0 < α < 1, where α is the mean of the random coefficient in the process. The asymptotic distribution of the test statistic is obtained. We compare the proposed test statistic with other statistics that can test serial dependence in time series of count via a typically numerical simulation, which indicates that our proposed test statistic has a good power.     

宁夏森林生态补偿政策创新与实践探索

在深入探索宁夏森林生态补偿实践历程的基础上,针对多元化补偿方式尚未形成、补偿标准偏低、补偿资金来源少、生态补偿管理部门职责交叉重复,以及生态补偿法律法规可操作性不强等问题,提出建立体制机制、制定系统的政策措施、完善法制保障体系、建立多元支付方式、提高公众生态保护意识等对策建议,为构建宁夏森林生态补偿制度和推动美丽新宁夏及西部地区生态文明先行区建设提供依据。     

农户参与退耕还林行为选择机理分析

退耕还林工程是迄今为止我国农户参与面最广的一项生态建设工程。该文首先分析了农户参与退耕还林行为选择的一般机理,并进行了农户参与退耕还林工程的成本与收益分析,进而指出：农户参与退耕还林的行为选择过程,多数情况下就是综合因素制约下的成本与收益之间进行权衡比较的过程,只有当预期总收益大于预期总成本时,农户才会参与退耕工程。     

海南省天然林保护工程实施影响实证分析

海南省是我国最早实施天然林保护工程的省份。本文通过一系列的调研工作,对海南省实施天然林保护工程以来森林资源、森工企业经营、企业职工状况、工程区居民生活改变等情况进行了详细描述,系统分析了海南省天然林保护工程实施的影响。文章认为海南省天然林保护工程的实施,取得了比较显著的生态效益,但对森工企业的经营、职工生活和社区居民生计方面产生了一些负面影响,应采取措施促进企业后续产业发展,改善职工生活。     

吉林省森林产权交易研究

森林产权交易是森林资产交易的核心。随着市场经济的发展,森林产权中的无形资产将越来越多,创新型森林产品将层出不穷,产权交易的重要性必将引起人们的重视。要振兴吉林省林业,必须认真研究森林产权交易,这也是深入落实林业科学发展观的实践步骤。笔者从森林产权交易的要素入手进行分析,认为森林产权交易必须规范化、市场化、法制化。     

The effect of the background risk in a simple chance improving decision model

We experimentally investigate the effect of an independent and exogenous background risk to initial wealth on subjects’ risk attitudes and explore an appropriate incentive mechanism when identical or similar tasks are repeated in an experiment. Taking a simple chance improving decision model under risk where the winning probabilities are negatively related to the potential gain, we find that such a background risk tends to make risk-averse subjects behave more risk aversely. Furthermore, we find that risk-averse subjects tend to show decreasing absolute risk aversion (DARA), and that a random round payoff mechanism (RRPM) would control the possible wealth effect. This suggests that RRPM would be a better incentive mechanism for an experiment where repetition of a task is used.

Single-step and multiple-step forecasting in one-dimensional single chirp signal using MCMC-based Bayesian analysis

Chirp signals are frequently used in different areas of science and engineering. MCMC-based Bayesian inference is done here for the purpose of one-step and multiple-step prediction in the case of the one-dimensional single chirp signal with iid error structure as well as dependent error structure with exponentially decaying covariances. We use Gibbs sampling technique and random walk MCMC to update the parameters. We perform total five simulation studies for the illustration purpose. We also do some real-data analysis to show how the method is working in practice.     

Random weighting estimation of sampling distributions via importance resampling

This paper presents a new random weighting-based adaptive importance resampling method to estimate the sampling distribution of a statistic. A random weighting-based cross-entropy procedure is developed to iteratively calculate the optimal resampling probability weights by minimizing the Kullback-Leibler distance between the optimal importance resampling distribution and a family of parameterized distributions. Subsequently, the random weighting estimation of the sampling distribution is constructed from the obtained optimal importance resampling distribution. The convergence of the proposed method is rigorously proved. Simulation and experimental results demonstrate that the proposed method can effectively estimate the sampling distribution of a statistic.     

Concurrent generation of binary and nonnormal continuous data through fifth-order power polynomials

Data collection process in most observational and experimental studies yield different types of variables, leading to the use of joint models that are capable of handling multiple data types. Evaluation of various statistical techniques that have been developed for mixed data in simulated environments requires concurrent generation of multiple variables. In this article, I present an important augmentation to a unified framework proposed in our previously published work for simultaneously generating binary and nonnormal continuous data given the marginal characteristics and correlation structure, via fifth-order power polynomials that are known to extend the area covered in the skewness-elongation plane and to provide a better approximation to the probability density function of the continuous variables. I evaluate how well the improved methodology performs in comparison to the original one, in a simulated setting with illustrations of algorithmic steps. Although the relative gains for the associational quantities are not substantial, the augmented version appears to better capture the marginal quantities that are pertinent to the higher-order moments, as indicated by very close resemblance between the specified and empirically computed quantities on average.     

PoisNor: An R package for generation of multivariate data with Poisson and normal marginals

In this article, the operational details of the R package PoisNor that is designed for simulating multivariate data with count and continuous variables with a prespecified correlation matrix are described, and examples of some important functions are given. The data-generation mechanism is a combination of the “NORmal To Anything” principle and a recently established connection between Poisson and normal correlations. The package provides a unique and useful tool that has been lacking for generating multivariate mixed data with Poisson and normal components.