Tests for Two-Sample Location Problem Based on Subsample Quantiles

This paper presents a new class of test procedures for two-sample location problem based on subsample quantiles. The class includes Mann-Whitney test as a special case. The asymptotic normality of the class of tests proposed is established. The asymptotic relative performance of the proposed class of test with respect to the optimal member of Xie and Priebe (2000) is studied in terms of Pitman efficiency for various underlying distributions.

Calculation of Two-Tailed Exact Probability in the Wald-Wolfowitz One-Sample Runs Test

The objectives of this paper are to demonstrate the algorithms employed by three statistical software programs (R, Real Statistics using Excel, and SPSS) for calculating the exact two-tailed probability of the Wald-Wolfowitz one-sample runs test for randomness, to present a novel approach for computing this probability, and to compare the four procedures by generating samples of 10 and 11 data points, varying the parameters n0 (number of zeros) and n1 (number of ones), as well as the number of runs. Fifty-nine samples are created to replicate the behavior of the distribution of the number of runs with 10 and 11 data points. The exact two-tailed probabilities for the four procedures were compared using Friedman’s test. Given the significant difference in central tendency, post-hoc comparisons were conducted using Conover’s test with Benjamini-Yekutielli correction. It is concluded that the procedures of Real Statistics using Excel and R exhibit some inadequacies in the calculation of the exact two-tailed probability, whereas the new proposal and the SPSS procedure are deemed more suitable. The proposed robust algorithm has a more transparent rationale than the SPSS one, albeit being somewhat more conservative. We recommend its implementation for this test and its application to others, such as the binomial and sign test.

Test of Normality of Waist Measurement Data of Young Male and Female Adults based on the Quantile - Quantile Plot

This paper investigates the normality of some real data set obtained from waist measurements of a group of 49 young adults. The quantile - quantile （Q-Q） plot and the analysis of correlation coefficients for the Q-Q plot is used to determine the normality or otherwise of the data set. In this regards, the probabilities of the quantiles were computed, modified and plotted. Thereafter the correlation coefficients for the quantile - quantile plots were obtained. Results indicate that at 0.1 level of significance, the data for young adult males of the sample were not normally distributed, and had a mean value that is within the range of low risk, healthwise, whereas the distribution of the data for young female adults showed reasonable normality, but also with a mean value that is within the range of low risk in terms of health condition.

基于时变传染网络和分位数Granger因果检验的系统性风险传染研究

基于18个经济体760家金融机构SRISK系统性风险数据,采用时变参数向量自回归模型和广义方差分解法测度全球系统性风险的时变传染网络,进而采用分位数Granger因果检验考察各经济体对中国高分位段系统性风险的影响。研究结果表明,结合SRISK数据和时变传染网络能有效识别重大风险事件的发生;发达和新兴经济体均会产生风险净输出效应,且会通过直接输出或香港地区间接输出风险至中国内地;在全样本与2008年金融危机、欧债危机、中美贸易战和新冠疫情重大风险事件期间,中国皆为风险净溢入国且新冠疫情期间溢入效应达历史最高;美英两国在重大风险事件期间均对中国高分位段系统性风险产生显著影响,新冠疫情期间中国高分位段系统性风险遭受外部冲击的形势最为严峻。

A Nonparametric Model Checking Test for Functional Linear Composite Quantile Regression Models

This paper is focused on the goodness-of-fit test of the functional linear composite quantile regression model.A nonparametric test is proposed by using the orthogonality of the residual and its conditional expectation under the null model.The proposed test statistic has an asymptotic standard normal distribution under the null hypothesis,and tends to infinity in probability under the alternative hypothesis,which implies the consistency of the test.Furthermore,it is proved that the test statistic converges to a normal distribution with nonzero mean under a local alternative hypothesis.Extensive simulations are reported,and the results show that the proposed test has proper sizes and is sensitive to the considered model discrepancies.The proposed methods are also applied to two real datasets.

Testing for Normality from the Parametric Seven-Number Summary

The objective of this study is to propose the Parametric Seven-Number Summary (PSNS) as a significance test for normality and to verify its accuracy and power in comparison with two well-known tests, such as Royston’s W test and D’Agostino-Belanger-D’Agostino K-squared test. An experiment with 384 conditions was simulated. The conditions were generated by crossing 24 sample sizes and 16 types of continuous distributions: one normal and 15 non-normal. The percentage of success in maintaining the null hypothesis of normality against normal samples and in rejecting the null hypothesis against non-normal samples (accuracy) was calculated. In addition, the type II error against normal samples and the statistical power against normal samples were computed. Comparisons of percentage and means were performed using Cochran’s Q-test, Friedman’s test, and repeated measures analysis of variance. With sample sizes of 150 or greater, high accuracy and mean power or type II error (≥0.70 and ≥0.80, respectively) were achieved. All three normality tests were similarly accurate;however, the PSNS-based test showed lower mean power than K-squared and W tests, especially against non-normal samples of symmetrical-platykurtic distributions, such as the uniform, semicircle, and arcsine distributions. It is concluded that the PSNS-based omnibus test is accurate and powerful for testing normality with samples of at least 150 observations.

A Simple Chi-Square Statistic for Testing Homogeneity of Zero-Inflated Distributions

Zero-inflated distributions are common in statistical problems where there is interest in testing homogeneity of two or more independent groups. Often, the underlying distribution that has an inflated number of zero-valued observations is asymmetric, and its functional form may not be known or easily characterized. In this case, comparisons of the groups in terms of their respective percentiles may be appropriate as these estimates are nonparametric and more robust to outliers and other irregularities. The median test is often used to compare distributions with similar but asymmetric shapes but may be uninformative when there are excess zeros or dissimilar shapes. For zero-inflated distributions, it is useful to compare the distributions with respect to their proportion of zeros, coupled with the comparison of percentile profiles for the observed non-zero values. A simple chi-square test for simultaneous testing of these two components is proposed, applicable to both continuous and discrete data. Results of simulation studies are reported to summarize empirical power under several scenarios. We give recommendations for the minimum sample size which is necessary to achieve suitable test performance in specific examples.

基于分位数映射法的四川省ECMWF模式降水预报误差订正分析

为做好ECMWF(European Centre for Medium-Range Weather Forecasting)模式本地化释用,提高四川省降水预报准确率,对四川省2020—2021年7—9月模式各量级降水预报系统性偏差规律分析发现,该模式预报的雨日较实况偏多,尤其是攀西地区和川西高原;预报的大雨日数盆地西南部及攀西地区多于实况,而盆地南部少于实况。然后,基于分位数映射法对模式预报的24 h累积降水开展大量级降水订正试验与检验。基于分位数映射法订正后,暴雨及以上量级TS(Threat Score)提高7%~15%,且各量级降水TS均高于多模式集成客观预报产品2%~4%,大雨及以上、暴雨及以上量级命中率提高10%~20%,订正后雨带位置特别是暴雨落区与实况更接近。

投资者高频情绪对股市成交量的异质性影响研究——基于分位数向量自回归模型

从高频视角分析股市情绪效应的异质性特征及机理,对我国金融风险管控具有重要意义。本文借助文本分析法抓取网络舆情数据,构造日内投资者高频情绪指数,在分位数Granger因果关系检验的基础上,构建分位数向量自回归模型并进行脉冲响应分析,探究不同极性投资者高频情绪对不同市场状态下及不同分位水平股市成交量的异质性影响。结果表明:(1)不同极性投资者情绪对股市成交量影响具有异质性,悲观情绪对股市成交量的脉冲强度明显大于乐观情绪且衰减较慢;(2)投资者情绪对股市成交量的影响随市场状态的变化而不同;(3)在相同市场状态下,情绪对不同分位水平股市成交量的影响也存在差异,投资者情绪对股市成交量的下分位点脉冲强度显著大于上分位点,中位点最弱。

Characterizing prediction errors of a new tree height model for cut-to-length Pinus radiata stems through the Burr TypeⅫdistribution

Unlike height-diameter equations for standing trees commonly used in forest resources modelling,tree height models for cut-to-length(CTL)stems tend to produce prediction errors whose distributions are not conditionally normal but are rather leptokurtic and heavy-tailed.This feature was merely noticed in previous studies but never thoroughly investigated.This study characterized the prediction error distribution of a newly developed such tree height model for Pin us radiata(D.Don)through the three-parameter Burr TypeⅫ(BⅫ)distribution.The model’s prediction errors(ε)exhibited heteroskedasticity conditional mainly on the small end relative diameter of the top log and also on DBH to a minor extent.Structured serial correlations were also present in the data.A total of 14 candidate weighting functions were compared to select the best two for weightingεin order to reduce its conditional heteroskedasticity.The weighted prediction errors(εw)were shifted by a constant to the positive range supported by the BXII distribution.Then the distribution of weighted and shifted prediction errors(εw+)was characterized by the BⅫdistribution using maximum likelihood estimation through 1000 times of repeated random sampling,fitting and goodness-of-fit testing,each time by randomly taking only one observation from each tree to circumvent the potential adverse impact of serial correlation in the data on parameter estimation and inferences.The nonparametric two sample Kolmogorov-Smirnov(KS)goodness-of-fit test and its closely related Kuiper’s(KU)test showed the fitted BⅫdistributions provided a good fit to the highly leptokurtic and heavy-tailed distribution ofε.Random samples generated from the fitted BⅫdistributions ofεw+derived from using the best two weighting functions,when back-shifted and unweighted,exhibited distributions that were,in about97 and 95%of the 1000 cases respectively,not statistically different from the distribution ofε.Our results for cut-tolength P.radiata stems represented the first case of any tree species where a non-normal error distribution in tree height prediction was described by an underlying probability distribution.The fitted BXII prediction error distribution will help to unlock the full potential of the new tree height model in forest resources modelling of P.radiata plantations,particularly when uncertainty assessments,statistical inferences and error propagations are needed in research and practical applications through harvester data analytics.

数字经济对劳动收入份额的影响效应——基于长江中游城市群的实证检验

本文以长江中游地区城市为例,选取2011—2021年长江中游地区52个城市面板数据,采用熵值法测算出每个城市的数字经济综合评价指数,并建立面板固定效应模型、分位数回归模型和机制检验模型实证检验数字经济对劳动收入份额的影响效应和作用机制,并进一步探讨了这一影响的产业异质性和边际效应差异。实证结果显示,数字经济能够显著抑制劳动收入份额的增长,且这一影响呈现出递增的边际效应,即随着劳动收入份额的增长,数字经济对劳动收入份额的抑制作用也逐渐明显。异质性分析表明,数字经济对不同产业的影响效应具有异质性,对第二、第三产业存在显著的抑制作用,但对第一产业的影响并不显著。机制检验表明,生产效率在数字经济对劳动收入份额的影响中起到了显著的负向调节作用,且生产效率越高,负向调节作用越大。

论文标题长度与被引频次的关系研究

标题是学术论文的画龙点睛之笔,学术界较少关注论文标题长度与被引频次之间的关系,有必要对其进行深入的研究。以《情报杂志》期刊2017年发表的论文为研究对象,综合采用Mann-Whitney U检验、回归分析和分位数回归研究论文标题长度和被引频次的关系。研究结果表明:标题长度与内容相关带来了其与论文被引频次相关;标题长度较短时论文的平均被引频次较高;较长标题论文的平均被引频次较低;论文标题长度与其被引频次无关。

内燃机故障信号特性分析及诊断策略

在内燃机上分别设置故障(气门响、挺杆响、销子响、敲缸响、小瓦响、大瓦响),测取机体上的振动信号。根据统计学理论,对振动信号分别进行假设检验,得出它们具有非平稳、非高斯、非线性特点,指出对其进行分析必须采用相适应的方法才能取得好的效果,并提出一系列诊断策略。

基于分位数回归的中国居民收入和消费的实证分析

阐述制约中国内需不足的几个主要因素,利用描述性统计和非参数方法重点考察中国居民收入差距的现状及其对扩大内需的重要影响。同时利用分位数回归技术对中国城镇和农村按收入等级划分的居民消费状况进行实证分析,结果表明:居民收入差距的确是制约中国内需不足的重要因素,不同收入阶层其边际消费倾向大不相同,并估算出这种消费倾向的具体值。从收入差距的视角提出了提高中国国内居民消费水平以便扩大内需的政策建议。

分位数回归技术综述

普通最小二乘回归建立了在自变量X=x下因变量Y的条件均值与X的关系的线性模型。而分位数回归(Quantile Regression)则利用自变量X和因变量Y的条件分位数进行建模。与普通的均值回归相比,它能充分反映自变量X对于因变量Y的分布的位置、刻度和形状的影响,有着十分广泛的应用,尤其是对于一些非常关注尾部特征的情况。文章介绍了分位数回归的概念以及分位数回归的估计、检验和拟合优度,回顾了分位数回归的发展过程以及其在一些经济研究领域中的应用,最后做了总结。

应用门限分位点回归模型估计条件VaR

在文献中,分位点回归模型是线性的,但是在实际中,这个假设不能很好地满足需要.为此提出了分位点回归的门限模型,用该模型实证分析了单只股票(浦东发展银行)的条件 VaR.选择了一种流动性风险指标作为条件,因此该条件 VaR 也可以看作是流动性调整的 VaR(La-VaR).经过实证分析发现,由门限分位点模型得到的结果能够更好地描述实际市场情况,也能更好地预测市场风险.

电商卖家信用评分的多因素校正模型及有效性检验——以淘宝网为例

针对电子商务环境中卖家信用得分计算方式的不足,提出了相应的改进策略:引入主营业务占比、开店时长和卖家的买家身份信用三个因素,建立卖家信用评分的多因素校正模型。更进一步,提出店铺的消费累积损失这一概念,并使用神经网络分位数回归模型和相应的评价指标,来验证改进卖家信用得分在解释店铺消费累积损失中的有效性。最后,对淘宝网信用评分实践进行研究,实证结果表明:卖家信用评分的多因素校正模型,不仅能够成功地解释店铺消费累积损失,而且能够更好地预测其未来取值变动规律,具有有效性。

基于广大极值分布的高频极值条件VaR模型

在考虑当前预期和波动性条件下,为了有效地捕获极端条件下收益率时间序列动态特征,提高VaR的度量精度,建立了基于高频数据的条件极值VaR模型。应用智能优化算法对条件极值分布的时变参数进行估计,考察了在不同样本容量分块下的条件极值VaR,并对VaR计算结果的精度进行了Kupiec-LR检验和动态分位数检验。研究结果表明,基于高频数据的条件极值分布较好地拟合了极端条件下的收益率特征,与McNeil提出的传统条件极值VaR相比,应用高频数据建立在条件广义极值分布基础上的条件极值VaR的Kupiec检验DQ检验值都较为理想,表明该模型能够捕捉到我国市场风险特征,提高极端情况下风险测度能力。

论文作者数与被引频次关系的再思考

[目的/意义]关于论文作者数与被引频次的关系研究,学术界得出的研究结论大相径庭,有必要继续进行探索。[方法/过程]以《图书情报工作》2015年发表的论文为例,综合采用独立样本t检验、回归分析、分位数回归研究了作者数与被引频次的关系。[结果/结论]单作者论文与多作者论文的平均被引频次没有显著差异;作者数虽然与被引频次正相关,但由于拟合优度太低,所以作者数对被引频次几乎没有影响;作者数与被引频次之间并不存在非线性关系;对于低被引频次的论文而言,作者数对被引频次的弹性更大,由于拟合优度太低,这种规律的体现并不显著;建议学术期刊影响力评价中取消平均作者数指标;该文提供了一种研究作者数与被引频次关系的系统研究范式。

指数投资组合的VaR模型及检验

文章采用ADCC模型和Riskmetrics方法,在估计中国四个主要股票指数市场相关性的基础上,利用不同权重投资组合收益构造正态分布下的VaR,并利用动态系数方法和失败率检验法进行VaR模型的准确性检验。结果表明,在等权重和最小方差投资组合权重下,用ADCC模型比用Riskmetrics方法进行投资组合和风险管理效果更好。