“全面二孩”后的人口预期与政策展望

“全面二孩”生育政策究竟能对人口发展产生怎样的影响？文章假定今后维持“全面二孩”人口政策不变进行了人口预测。结果可知：城乡全面放开二孩政策并不会带来出生率的猛增和人口数量的剧烈反弹,高峰期最多可以新增出生人口400万;政策调整会提高人口峰值,有利于改善人口结构,延缓劳动力供给的衰减速度,但作用微乎其微。从其他国家的人口发展经验看,生育率的下降趋势似乎不可避免。因此,“全面二孩”并不是生育政策调整的终点,应继续监测生育水平,进一步调整生育政策;建立和完善“家庭友好”政策体系;从社会体制和经济体制应对负面影响。     

基于神经网络的天津市城市化水平分析及预测

针对一般城市化水平分析只侧重人口指标测定的不足,在比较了灰色预测与神经网络预测优劣的基础上,以天津市为例,采用误差逆传播人工神经网络(简称BP神经网络)方法对人口、产业以及人居环境三项城市化水平指标做出短期预测,在与北京、上海等城市的同一指标的比较后得出天津市当前及未来的城市化综合发展指标,并据此提出了相关建议。     

邓小平社会主义本质论的逻辑内涵、解释与预测功能

文章通过比较邓小平对社会主义本质论概括与马克思对未来社会主义社会基本特征描述的逻辑推演,发现两者之间是一脉相承的,都是运用生产力与生产关系的基本原理,特别是生产资料所有制理论来分析社会形态本质特征的。邓小平对社会主义本质论的概括比马克思对未来社会主义的特征描述更具有针对性、现实性和指导性;邓小平社会主义本质论的科学性体现在它对当今的所有制和收入分配改革仍然具有很强的解释和预测功能。     

钢管自动跟踪及在线自动定长切割控制器的软硬件设计

采用单片机实现了焊管机生产线钢管自动定长切割的控制，并使其具有结构小、功能强控制精度高等优点；介绍了控制器的软硬件设计思想和设计方法．     

刑事搜查立法精密化比较研究

刑事搜查制度既关系到刑事侦查权的正当行使,又关系到被搜查人的人权保护,各国立法者都要在打击犯罪和保障人权之间进行艰难的价值选择,因此西方法治国家的立法者设计了一套精密科学的机制。然而我国关于刑事搜查制度方面的立法粗糙,人权保障不足。借鉴域外经验对我国刑事搜查制度予以全面改造,才是明智之举。     

一种考虑基差非对称影响的期货波动性预测模型研究——基于上海铜期货市场的实证分析

研究了基差对上海铜期货收益波动率影响的非对称效应。实证结果表明,基差对铜期货的收益波动存在显著的非对称影响,其中负基差对波动性的影响要明显大于正的基差项。通过与GARCH 模型和未考虑基差项的SEGARCH模型对铜期货的样本外预测能力的比较表明,考虑基差对波动性的非对称影响的AE-GARCH模型能显著减小铜期货波动性预测的误差。     

Non-asymptotic sequential confidence regions with fixed sizes for the multivariate nonlinear parameters of regression

In this paper we consider a sequential design for the estimation of nonlinear parameters of regression with guaranteed accuracy. Non-asymptotic confidence regions with fixed sizes for the least squares estimates are used. The obtained confidence region is valid for finite numbers of data points when the distributions of the observations are unknown.     

水质预报系统的MPI+OpenMP并行优化研究

OpenMP(Open Multi-Processing)能够在共享存储环境中获得理想的并行性能,MPI＋OpenMP混合并行编程模型更贴近于多核心集群的体系结构。在多核心集群上进行实验,混合模型相对串行程序可缩短95%的运算时间,加速比超过20。实验结果表明,该混合模型比纯MPI并行的系统具有更优的加速比和扩展性,是对纯MPI并行系统的一种改进。     

Estimation and Inference Procedures for Semiparametric Distribution Models with Varying Linear‐Index

More flexible semiparametric linear‐index regression models are proposed to describe the conditional distribution. Such a model formulation captures varying effects of covariates over the support of a response variable in distribution, offers an alternative perspective on dimension reduction and covers a lot of widely used parametric and semiparameteric regression models. A feasible pseudo likelihood approach, accompanied with a simple and easily implemented algorithm, is further developed for the mixed case with both varying and invariant coefficients. By justifying some theoretical properties on Banach spaces, the uniform consistency and asymptotic Gaussian process of the proposed estimator are also established in this article. In addition, under the monotonicity of distribution in linear‐index, we develop an alternative approach based on maximizing a varying accuracy measure. By virtue of the asymptotic recursion relation for the estimators, some of the achievements in this direction include showing the convergence of the iterative computation procedure and establishing the large sample properties of the resulting estimator. It is noticeable that our theoretical framework is very helpful in constructing confidence bands for the parameters of interest and tests for the hypotheses of various qualitative structures in distribution. Generally, the developed estimation and inference procedures perform quite satisfactorily in the conducted simulations and are demonstrated to be useful in reanalysing data from the Boston house price study and the World Values Survey.     

Comparison of the effectiveness of forecasts obtained by means of selected probability functions with respect to forecast error distributions

The forecasting of sales in a company is one of the crucial challenges that must be faced. Nowadays, there is a large spectrum of methods that enable making reliable forecasts. However, sometimes the nature of time series excludes many well-known and widely used forecasting methods (e.g., econometric models). Therefore, the authors decided to forecast on the basis of a seasonally adjusted median of selected probability distributions. The obtained forecasts were verified by means of distributions of the Theil U² coefficient and unbiasedness coefficient.