桥式起重机轻量化设计的关键要素

伴随着科学技术的不断发展与进步,我国目前在起重机的设计上正逐步朝着轻量化的方向发展。文章从对计算方法以及设计进行改进、使用轻质的材料、对结构进行改进、对机构进行改进、从合理使用材料以及减轻电气系统等问题出发,对桥式起重机轻量化设计的关键要素进行了探究。     

基于数据分析下我国电子商务的发展探究

本文主要通过使用数据分析的方法,对我国电子商务发展进行探究,分析电商行业近几年快速发展的原因,并探讨电子商务发展中存在的问题。     

关于档案管理现代化问题的探究

对正确认识档案管理的地位与价值进行分析,从方案管理安全性与使用阶段对社会发展的促进作用来进行。重点探讨档案管理向现代化方面发展的途径,对电子档案管理系统的建设方法进行论述,帮助全面落实现代化档案管理技术。     

浅谈轮式拖拉机保养及正确使用

随着社会的进步和科技的不断发展,在我国农业作业中轮式拖拉机使用也越来越普遍,当前轮式拖拉机使用者往往还停留在单纯的驾驶上面,对正确使用和保养的重视程度不够。以下描述了如何对轮式拖拉机进行保养,指出正确使用轮式拖拉机的方法。     

模糊数学在企业绩效评价中的应用浅析

作为一种对模糊因素进行科学量化的评价方法,模糊数学综合评价方法被广泛使用在企业的绩效评价体系之中,为企业对自身经营发展状况进行客观评估提供了重要的评价手段。本文对模糊数学在企业绩效评价过程中的应用作简要探讨。     

金属切削加工中的表面质量控制策略研究

随着我国社会经济的不断发展,金属在我国广泛使用,人们对金属加工中表面质量的要求有所提高。本文对金属切削加工的表面质量进行有效的控制进行探究,旨在找到控制金属切削质量的有效方法。     

DEA方法在财政支出有效性评价中的应用解析

随着我国的改革开放与现代化建设的深入发展,促进了我国经济腾飞的同时,也推动了各大企业的发展,而企业的发展离不开的财政收入与支出的预算及决策,只有形成一种新的理念和方法才能对其财政支出进行有效性的评价,而DEA方法可以使用数学模型,对企业复合型生产中所产生的多项输入与输出值进行决策与分析,以达到企业发展的需求,本文从对DEA方法的理解入手,对DEA方法评价的特点及应用进行了深入的研究与分析。     

人力资源管理绩效审计评价模型分析

在对人力资源管理绩效进行审计评价时,定量指标的使用是对定性方法评价的必要补充。本文引入了人力资源投入结构理论和当期群体价值、人力资源贡献率指标,分别对人力资源管理的投入、产出和效率进行绩效定量评价的方法进行了探讨,希望能够以此促进人力资源管理审计的发展。     

大数据时代作业成本法在环境成本管理中的应用

随着科技的发展,大数据的发展已经开始影响着我们的生产生活。目前,部分企业在进行环境成本管理时要么没有使用规范的方法,要么在使用规范方法时存在许多错误。在大数据时代下,我们可以根据企业的物质流来对企业环境成本的项目进行监测与分析,结合大数据手段来进行成本管理流程构建,并将其应用至企业环境成本的管理之中,这样才能促进环境成本管理的有效性。     

软基处理在公路工程施工中的应用分析

随着我国基础性建设规模的不断加大,在软土地基上修建公路已经极为普遍.然而,软土地基强度低、固结慢、压缩性大等特点使其不能作为天然地基直接使用,必须采用适当的方式进行处理方可使用.文章主要介绍了几种常见的软土地基处理方法,并对选择软土地基处理方法时应考虑的因素进行了深入分析,以期对我国公路工程事业的发展有所裨益.     

Data snooping in equity premium prediction

We analyze the performance of a comprehensive set of equity premium forecasting strategies. All strategies were found to outperform the mean in previous academic publications. However, using a multiple testing framework to account for data snooping, our findings support Welch and Goyal (2008) in that almost all equity premium forecasts fail to beat the mean out-of-sample. Only few forecasting strategies that are based on Ferreira and Santa-Clara’s (2011) sum-of-the-parts approach generate robust and statistically significant economic gains relative to the historical mean even after controlling for data snooping and accounting for transaction costs.     

Estimation of a proportion in survey sampling using the logratio approach

The estimation of a mean of a proportion is a frequent task in statistical survey analysis, and often such ratios are estimated from compositions such as income components, wage components, tax components, etc. In practice, the weighted arithmetic mean is regularly used to estimate the center of the data. However, this estimator is not appropriate if the ratios are estimated from compositions, because the sample space of compositional data is the simplex and not the usual Euclidean space. We demonstrate that the weighted geometric mean is useful for this purpose. Even for different sampling designs, the weighted geometric mean shows excellent behavior.     

Regression toward the mean in effectiveness studies: theoretically possible,not mathematically inevitable

Regression Toward the Mean (RTM) in extreme-group quasi-experiments is a thoroughly theoretical matter, not a simple mathematical necessity, so results of extreme-group before-and-after treatment effectiveness studies should not be doubted simply because of the theoretical possibility of RTM. The estimation of RTM, wherever one has a treatment group with a pretreatment mean off to one side of its population’s mean, requires knowledge of the population mean, standard deviation, and regression slope. RTM ought not be presumed to be operative in studies unless its several theoretical assumptions have been justified and its implications for sample data have been corroborated on that data. RTM can properly be adjusted for only when it has been adequately estimated from the relevant population data.     

Estimation in Two-Stage Models with Heteroscedasticity

A surprising number of important problems can be cast in the framework of estimating a mean and variance using data arising from a two-stage structure. The first stage is a random sampling of "units" with some quantity of interest associated with the unit. The second stage produces an estimate of that quantity and usually, but not always, an estimated standard error, which may change considerably across units. Heteroscedasticity in the estimates over different units can arise for a number of reasons, including variation associated with the unit and changing sampling effort over units. This paper presents a broad discussion of the problem of making inferences for the population mean and variance associated with the unobserved true values at the first stage of sampling. A careful discussion of the causes of heteroscedasticity is given, followed by an examination of ways in which inferences can be carried out in a manner that is robust to the nature of the within unit heteroscedasticity. Among the conclusions are that under any type of heteroscedasticity, an unbiased estimate of the mean and the variance of the estimated mean can be obtained by using the estimates as if they were true unobserved values from the first stage. The issue of using the mean versus a weighted average which tries to account for the heteroscedasticity is also discussed. An unbiased estimate of the population variance is given and the variance of this estimate and its covariance with the estimated mean is provided under various types of heteroscedasticity. The two-stage setting arises in many contexts including the one-way random effects models with replication, meta-analysis, multi-stage sampling from finite populations and random coefficients models. We will motivate and illustrate the problem with data arising from these various contexts with the goal of providing a unified framework for addressing such problems.     

Bias in the estimation of the mean reversion parameter in continuous time models

It is well known that for continuous time models with a linear drift standard estimation methods yield biased estimators for the mean reversion parameter both in finite discrete samples and in large in-fill samples. In this paper, we obtain two expressions to approximate the bias of the least squares/maximum likelihood estimator of the mean reversion parameter in the Ornstein–Uhlenbeck process with a known long run mean when discretely sampled data are available. The first expression mimics the bias formula of Marriott and Pope (1954) for the discrete time model. Simulations show that this expression does not work satisfactorily when the speed of mean reversion is slow. Slow mean reversion corresponds to the near unit root situation and is empirically realistic for financial time series. An improvement is made in the second expression where a nonlinear correction term is included into the bias formula. It is shown that the nonlinear term is important in the near unit root situation. Simulations indicate that the second expression captures the magnitude, the curvature and the non-monotonicity of the actual bias better than the first expression.     

Distribution-free tests of mean vectors and covariance matrices for multivariate paired data

We study a permutation procedure to test the equality of mean vectors, homogeneity of covariance matrices, or simultaneous equality of both mean vectors and covariance matrices in multivariate paired data. We propose to use two test statistics for the equality of mean vectors and the homogeneity of covariance matrices, respectively, and combine them to test the simultaneous equality of both mean vectors and covariance matrices. Since the combined test has composite null hypothesis, we control its type I error probability and theoretically prove the asymptotic unbiasedness and consistency of the combined test. The new procedure requires no structural assumption on the covariances. No distributional assumption is imposed on the data, except that the permutation test for mean vector equality assumes symmetric joint distribution of the paired data. We illustrate the good performance of the proposed approach with comparison to competing methods via simulations. We apply the proposed method to testing the symmetry of tooth size in a dental study and to finding differentially expressed gene sets with dependent structures in a microarray study of prostate cancer.     

Identifying regime changes in closed-end fund discounts

In seeming contradiction of the efficient markets hypothesis, closed-end fund shares typically trade at discounts to their portfolio values. We find that about half of these discounts are nonstationary. Focusing only on those funds that have stationary discounts, this study applies the Bai and Perron (1998, 2003a,b) methodology to test for structural breaks in the mean discounts. Virtually all have structural breaks, and our findings contradict previous studies that indicate closed-end fund discounts revert to a long-term mean value. The data indicate that closed-end fund trading strategies are more risky than they superficially appear. As structural breaks in mean discounts do not occur together, our analysis does not find support for a common factor (possibly investor sentiment) causing these breaks.     

Scoring functions for ordered classifications in statistical analysis

The problem of scoring ordered classifications prior to the further statistical analysis is discussed. A review of some methods of scoring is provided. This includes linear transformations of integer scores, where previous applications to two way classifications are introduced. Also reviewed are scores based on canonical correlations, maximum likelihood scores under assumed logistic distributions for variables, ridits, and conditional mean scoring functions. The latter are shown to satisfy a reasonable set of postulates, and demonstrates that some earlier attempts to do this were incomplete. Examples of the conditional mean scoring function under different distributional assumptions are given. Methods based on compounded functions of proportions for categorical data are applied to many of the scores reviewed and introduced. Appropriate algorithms for these methods are introduced and exemplified. Through the medium of a range of existing data sets the sensitivity of their results to differing scoring systems applied to two way classifications is examined. It is seen that apart from data arising from highly skewed distributions little is to be lost by using simple integer scores.     

Analysis of Hybrid Life-tests in Presence of Competing Risks

The mixture of type-I and type-II censoring schemes, called the hybrid censoring scheme is quite common in life-testing or reliability experiments. In this paper, we consider the competing risks model in presence of hybrid censored data. Under this set up, it is assumed that the item may fail due to various causes and the corresponding lifetime distributions are independent and exponentially distributed with different scale parameters. We obtain the maximum likelihood estimators of the mean life of the different causes and derive their exact distributions. Using the exact distributions, all the moments can be obtained. Asymptotic confidence intervals and two bootstrap confidence intervals are also proposed. Bayes estimates and credible intervals of the unknown parameters are obtained under the assumptions of independent inverted gamma priors of the mean life of the different causes. Different methods have been compared using Monte Carlo simulations. Onereal data set has been analyzed for illustrative purposes. Part of the work was supported by a grant from the Natural Sciences and Engineering Research Council.     

Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices

In this article we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price; hence, we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by ordinary least squares and the elastic net, and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component in a mean reverting jump diffusion model in the residuals. The models’ forecasts are evaluated with use of four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included. It is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure to properly forecast correlated time series.