双线自闭区段客货列车速度比对扣除系数的影响分析

目前扣除系数的计算方法主要为分析法与模拟法，扣除系数的常见影响因素有客货列车速度比、区段均衡度、追踪间隔时分等。通过模拟法进行不同客货列车速度比下的运行图铺画，得出客货列车速度比对扣除系数的影响规律分析。实验表明：随着客货速度比的增加，扣除系数将逐渐变大，据此建议在相关双线客货混跑区段，选取合适的客货速度比，以减小对运行图通过能力的影响     

从想象、走进到反思:人类学视野下的“边缘”研究谱系

人类学视野下的"边缘"不仅是与"中心"相对的空间范畴,也是一种反思殖民主义、后殖民主义、西方霸权、现代性以及全球化的话语和研究范式。在从想象、走进到反思的演变过程中,"边缘"研究经历了从宏观到微观、从猜测到实证、从西方中心主义到多元化、从全球化到本土化的转换。     

固定效应部分线性变系数面板模型的快速有效估计

本文将最小二乘支持向量机(LSSVM) 和二次推断函数法(QIF) 相结合，为个体内具有相关结构的固定效应部分线性变系数面板模型提供了一种新的快速估计方法；在一定的正则条件下，论证了参数估计量的渐近正态性和非参数估计量的收敛速度；采用Monte Carlo模拟考察了估计方法在有限样本下的表现并将估计技术应用于现实数据分析。该方法不仅保证了估计的有效性和统计推断力，而且程序运行速度得到较大幅度提升。     

Stochastic properties of the mixed accelerated hazard models

The accelerated hazard model in survival analysis assumes that the covariate effect acts the time scale of the baseline hazard rate. In this paper, we study the stochastic properties of the mixed accelerated hazard model since the covariate is considered basically unobservable. We build dependence structure between the population variable and the covariate, and also present some preservation properties. Using some well-known stochastic orders, we compare two mixed accelerated hazards models arising out of different choices of distributions for unobservable covariates or different baseline hazard rate functions.     

Robust Bayesian nonlinear mixed‐effects modeling of time to positivity in tuberculosis trials

Early phase 2 tuberculosis (TB) trials are conducted to characterize the early bactericidal activity (EBA) of anti‐TB drugs. The EBA of anti‐TB drugs has conventionally been calculated as the rate of decline in colony forming unit (CFU) count during the first 14 days of treatment. The measurement of CFU count, however, is expensive and prone to contamination. Alternatively to CFU count, time to positivity (TTP), which is a potential biomarker for long‐term efficacy of anti‐TB drugs, can be used to characterize EBA. The current Bayesian nonlinear mixed‐effects (NLME) regression model for TTP data, however, lacks robustness to gross outliers that often are present in the data. The conventional way of handling such outliers involves their identification by visual inspection and subsequent exclusion from the analysis. However, this process can be questioned because of its subjective nature. For this reason, we fitted robust versions of the Bayesian nonlinear mixed‐effects regression model to a wide range of TTP datasets. The performance of the explored models was assessed through model comparison statistics and a simulation study. We conclude that fitting a robust model to TTP data obviates the need for explicit identification and subsequent “deletion” of outliers but ensures that gross outliers exert no undue influence on model fits. We recommend that the current practice of fitting conventional normal theory models be abandoned in favor of fitting robust models to TTP data.     

中泰铁路面临的挑战及对策分析

从英拉政府时期的"高铁换大米"方案到巴育军政府时期的新铁路方案,中泰铁路前后经历了中泰高铁缘起、中泰高铁曲折发展、中泰高铁向中泰铁路转变、中泰铁路发展筹备、中泰铁路发展陷入困境和中泰铁路发展缓慢推进等六个阶段的波折变化.中泰两国关于方案本身的分歧,日本高铁的外部竞争和冲击,以及泰国国内政权更迭频繁等因素是造成中泰铁路一波三折的主要原因.基于对中泰铁路建设"好事多磨"的全面探讨和研究,提出构建政治风险评估和预警机制、创新企业投融资合作模式、加强舆论宣传作用等建议,助力中泰铁路项目平稳推进.     

欧洲债务危机的主要成因分析

肇始于希腊的欧洲债务危机到目前为止都没有停息的迹象,甚至还呈蔓延之势。欧洲债务危机爆发的直接原因是在金融危机的背景下,政府支出规模持续扩大,导致财政赤字居高不下,最终使得国债规模超过政府的偿还能力,出现明显的违约可能性。同时,国际竞争力不足,导致出口受阻,使得资金问题更加严重。从更深层次看,产业结构不合理,经济发展速度缓慢,过低的投资率和储蓄率及过高的储蓄投资转化率,都是导致欧债危机的重要原因。     

照相记

第一次给七奶一家照相的过程是这样的。 五叔从屋子里出来小声对我说：说是给全家照，其实是为你七奶照，预备着，明白了吧？随后就回屋去了。     

Implementing Monte Carlo tests with p-value buckets

Software packages usually report the results of statistical tests using p-values. Users often interpret these values by comparing them with standard thresholds, for example, 0.1, 1, and 5%, which is sometimes reinforced by a star rating (***, **, and *, respectively). We consider an arbitrary statistical test whose p-value p is not available explicitly, but can be approximated by Monte Carlo samples, for example, by bootstrap or permutation tests. The standard implementation of such tests usually draws a fixed number of samples to approximate p. However, the probability that the exact and the approximated p-value lie on different sides of a threshold (the resampling risk) can be high, particularly for p-values close to a threshold. We present a method to overcome this. We consider a finite set of user-specified intervals that cover [0, 1] and that can be overlapping. We call these p-value buckets. We present algorithms that, with arbitrarily high probability, return a p-value bucket containing p. We prove that for both a bounded resampling risk and a finite runtime, overlapping buckets need to be employed, and that our methods both bound the resampling risk and guarantee a finite runtime for such overlapping buckets. To interpret decisions with overlapping buckets, we propose an extension of the star rating system. We demonstrate that our methods are suitable for use in standard software, including for low p-value thresholds occurring in multiple testing settings, and that they can be computationally more efficient than standard implementations.