建国初期毛泽东人民政协思想的伟大意义

中国人民政治协商会议是中国共产党领导的统一战线组织.毛泽东不仅亲自组织、领导建立了人民政协,而且在长期领导和指导政协工作的过程中,科学地阐明了人民政协的性质、确立了人民政协的基本职能、培育了人民政协的优良作风.     

论电子商务法基本原则

电子商务法学界对电子商务法基本原则的认识仍存在种种分歧和不足.技术中立原则只是民法平等原则的细化,不能成为电子商务法基本原则.在梳理国内现有理论研究并汲取合理成分的基础上,以法律基本原则的原理为基础,从电子商务法解决的核心问题是在非面对面交易环境中,如何确保当事人能够获得商品和价款出发,提出电子商务法基本原则是安全原则.功能等同是判定利用新型信息技术所实施的法律行为有效与否的标准,是安全原则的重要外延和具体化.安全原则要求第三方电子商务平台针对平台内发生的第三方侵权行为负有合理的注意义务.在安全原则指引下,技术中立非绝对,应包含国家安全等例外规则.     

An Adaptive Functional Autoregressive Forecast Model to Predict Electricity Price Curves

We propose an adaptive functional autoregressive (AFAR) forecast model to predict electricity price curves. With time-varying operators, the AFAR model can be safely used in both stationary and nonstationary situations. A closed-form maximum likelihood (ML) estimator is derived under stationarity. The result is further extended for nonstationarity, where the time-dependent operators are adaptively estimated under local homogeneity. We provide theoretical results of the ML estimator and the adaptive estimator. Simulation study illustrates nice finite sample performance of the AFAR modeling. The AFAR model also exhibits a superior accuracy in the forecast exercise of the California electricity daily price curves compared to several alternatives.     

Uniform in bandwidth consistency for various kernel estimators involving functional data

The paper investigates various nonparametric models including regression, conditional distribution, conditional density and conditional hazard function, when the covariates are infinite dimensional. The main contribution is to prove uniform in bandwidth asymptotic results for kernel estimators of these functional operators. Then, the application issues, involving data-driven bandwidth selection, are discussed.     

Functional analysis of high-content high-throughput imaging data

High-content automated imaging platforms allow the multiplexing of several targets simultaneously to generate multi-parametric single-cell data sets over extended periods of time. Typically, standard simple measures such as mean value of all cells at every time point are calculated to summarize the temporal process, resulting in loss of time dynamics of the single cells. Multiple experiments are performed but observation time points are not necessarily identical, leading to difficulties when integrating summary measures from different experiments. We used functional data analysis to analyze continuous curve data, where the temporal process of a response variable for each single cell can be described using a smooth curve. This allows analyses to be performed on continuous functions, rather than on original discrete data points. Functional regression models were applied to determine common temporal characteristics of a set of single cell curves and random effects were employed in the models to explain variation between experiments. The aim of the multiplexing approach is to simultaneously analyze the effect of a large number of compounds in comparison to control to discriminate between their mode of action. Functional principal component analysis based on T-statistic curves for pairwise comparison to control was used to study time-dependent compound effects.     

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.     

Multiple comparisons in two-factor testing of psychophysical thresholds by the delta test method

We propose an algorithm of multiple comparisons in two-factor testing of psychophysical thresholds. We use logistic regression combined with guessing rate and adopt the step-down procedure with Ryan–Einot–Gabriel–Welsch (REGW) significance levels for multiple test. We test hypotheses of main effects and interaction in two-factor problem by using the delta test statistics.     

A simulation study of the Bennett test and Miller test for coefficients of variation

In the article, properties of the Bennett test and Miller test are analyzed. Assuming that the sample size is the same for each sample and considering the null hypothesis that the coefficients of variation for k populations are equal against the hypothesis that k ? 1 coefficients of variation are the same but differ from the coefficient of variation for the kth population, the empirical significance level and the power of the test are studied. Moreover, the dependence of the test statistic and the power of the test on the ratio of coefficients of variation are considered. The analyses are performed on simulated data.     

M-estimation for functional linear regression

This paper studies M-estimation in functional linear regression in which the dependent variable is scalar while the covariate is a function. An estimator for the slope function is obtained based on the functional principal component basis. The global convergence rate of the M-estimator of unknown slope function is established. The convergence rate of the mean-squared prediction error for the proposed estimators is also established. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedure. Finally, the proposed method is applied to analyze the Berkeley growth data.     

Mitigating the Impact of Functional Dominance in Cross‐Functional Process Improvement Teams

Manufacturing process improvement teams often draw upon cross‐functional expertise. However, teams dominated by a single or few functions often do not achieve the desired interdisciplinary learning and cooperation, which in turn could negatively affect the performance of that cross‐functional team. Through an empirical analysis of 149 manufacturing process improvement teams in six SIC industrial categories, we show that even after controlling for several factors such as team size, number of functions, gender diversity, ethnicity, number of management levels, and geographic dispersion of team members, psychological safety of work environment fully mediates the relationship between functional dominance and team performance. We also confirm that a team leader possessing high interpersonal justice mitigates the deficiency of dominated teams and improves team performance by fostering a psychologically safe work environment. Our recommendation therefore is that when a dominated team is unavoidable due to the needed skill sets or other organizational considerations, to ensure success it is imperative that the firm chooses a team leader who can promote psychological safety by being just, and who can effectively encourage input from all functional team members and give them objective feedback.