Density estimation in the uniform deconvolution model

We consider the problem of estimating a probability density function based on data that are corrupted by noise from a uniform distribution. The (nonparametric) maximum likelihood estimator for the corresponding distribution function is well defined. For the density function this is not the case. We study two nonparametric estimators for this density. The first is a type of kernel density estimate based on the empirical distribution function of the observable data. The second is a kernel density estimate based on the MLE of the distribution function of the unobservable (uncorrupted) data.     

Generalized piecewise linear histograms

We extend the concept of piecewise linear histogram introduced recently by Beirlant, Berlinet and Györfi. The disadvantage of that histogram is that in many models it takes on negative values with probability close to 1. We show that for a wide set of models, the extended class of estimates contains a bona fide density with probability tending to 1 as the sample size n increases to infinity. The mean integrated absolute error in the extended class of estimators decreases with the same rate n^–2/5 as in the original narrower class.     

Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite-dimensional parameter spaces.     

The topography of metropolitan employment: Identifying centers of employment in a polycentric urban area

While it is well recognized that US metropolitan areas are polycentric, there is little consensus as to the appropriate method for identifying concentrations of employment within them. Existing methods suffer from strong assumptions about parametric form, misspecification, or reliance on local knowledge to calibrate model parameters. This paper introduces a new nonparametric method for identifying subcenters. Results indicate that this, more flexible, nonparametric approach yields greater accuracy with regard to both urban and suburban centers compared with other approaches. This approach should provide better data for the numerous topics that depend on the spatial accounting of employment within metropolitan areas.     

A semiparametric GARCH model for foreign exchange volatility

A semiparametric extension of the GJR model (Glosten et al., 1993. Journal of Finance 48, 1779–1801) is proposed for the volatility of foreign exchange returns. Under reasonable assumptions, asymptotic normal distributions are established for the estimators of the model, corroborated by simulation results. When applied to the Deutsche Mark/US Dollar and the Deutsche Mark/British Pound daily returns data, the semiparametric volatility model outperforms the GJR model as well as the more commonly used GARCH(1,1) model in terms of goodness-of-fit, and forecasting, by correcting overgrowth in volatility.     

基于遗传算法的核函数参数优化

核主分量分析是一种输入输出特征非线性变换技术。选择最优或接近最优的非线性变换核函数参数,使类的可分性测度最大,是KPCA应用于特征提取的关键。本文采用高斯变异遗传算法作优化技术,实现了KPCA和GA的集成,适合核函数参数的优化选择。仿真表明,该技术可行、有效。     

Regional, national, and spatially explicit scenarios of demographic and economic change based on SRES

We report here spatially explicit scenario interpretations for population and economic activity (GDP) for the time period 1990 to 2100 based on three scenarios (A2, B1, and B2) from the IPCC Special Report on Emissions Scenarios (SRES). At the highest degree of spatial detail, the scenario indicators are calculated at a 0.5 by 0.5 degree resolution. All three scenarios follow the qualitative scenario characteristics, as outlined in the original SRES scenarios. Two scenarios (B1 and B2) also follow (with minor adjustments due to scenario improvements) the original SRES quantifications at the level of 4 and 11 world regions respectively. The quantification of the original SRES A2 scenario has been revised to reflect recent changing perceptions on the demographic outlook of world population growth. In this revised “high population growth” scenario, A2r world population reaches some 12 billion by 2100 (as opposed to some 15 billion in the original SRES A2 scenario) and is characterized by a “delayed fertility transition” that is also mirrored in a delayed (economic) development catch-up, resulting in an initially stagnating and subsequently only very slow reduction in income disparities. The spatially explicit scenario interpretation proceeds via two steps. Through a combination of decomposition and optimization methods, world regional scenario results are first disaggregated to the level of 185 countries. In a subsequent second step, national results are further disaggregated to a grid cell level, taking urbanization and regional (rural–urban) income disparities explicitly into account. A distinguishing feature of the spatially explicit scenario results reported here is that both methodologies, as well as numerical assumptions underlying the “downscaling” exercise, are scenario-dependent, leading to distinctly different spatial patterns of population and economic activities across the three scenarios examined.     

Land conservation and tenure security in Kenya: Boserup's hypothesis revisited

Land conservation technologies used by farmers are known to play an important role in improving farm incomes and household welfare in the long run. For this reason substantial investments have been made in research to improve agricultural technologies in various parts of the world, from the development of new crop varieties to new practices of land management. This paper explores the impact of land rights among other factors on adoption of soil and water conservation practices. The study further tests for Boserup's hypothesis (correlation between population density, land conservation and property rights) using panel survey data collected from farming households. The key findings of the paper are that property right regimes and population density affect both the decision to conserve land as well as the type of conservation practices used by farmers. The results further suggest a positive correlation between land tenure security and population density, thus supporting Boserup's hypothesis. The findings call for pursuit of both short-term and long-term policy measures that offer incentives for land conservation through government initiatives and participation of local communities.     

Distribution Dynamics of CO<Subscript>2</Subscript> Emissions

This paper uses nonparametric methods to examine the convergence in CO₂ emissions per capita on a sample of 100 countries for the period 1966–1996. Industrial countries show a convergence pattern. However, there is little evidence of convergence for the whole sample.     

磁放大器式硅弧焊整流器控制特性分析

本文详细分析了三相内反馈磁放大器式硅弧焊整流器的控制特性,给出了各铁心的磁通密度变化规律及输出电压 U_o 与控制电流 I_K 之间的数学表达式。对确定内反馈磁放大器式硅弧焊整流器一些参数有一定的指导意义。