近年居民消费结构统计分析的研究综述——关于因子分析和聚类分析的应用

本文对近年有关居民消费结构的实证分析进行研究,简述了数据选取、实证方法的变革与演进;回顾了模型分析的过程与结论;并且进行了相应的评述、提出了建议,以期为今后统计实证手段的应用及后续研究提供相应参考。     

Asymptotic properties of the realized skewness and related statistics

Annals of the Institute of Statistical Mathematics - The recent empirical works have pointed out that the realized skewness, which is the sample skewness of intraday high-frequency returns of a...     

A high-dimensional Wilks phenomenon

A theorem by Wilks asserts that in smooth parametric density estimation the difference between the maximum likelihood and the likelihood of the sampling distribution converges toward a Chi-square distribution where the number of degrees of freedom coincides with the model dimension. This observation is at the core of some goodness-of-fit testing procedures and of some classical model selection methods. This paper describes a non-asymptotic version of the Wilks phenomenon in bounded contrast optimization procedures. Using concentration inequalities for general functions of independent random variables, it proves that in bounded contrast minimization (as for example in Statistical Learning Theory), the difference between the empirical risk of the minimizer of the true risk in the model and the minimum of the empirical risk (the excess empirical risk) satisfies a Bernstein-like inequality where the variance term reflects the dimension of the model and the scale term reflects the noise conditions. From a mathematical statistics viewpoint, the significance of this result comes from the recent observation that when using model selection via penalization, the excess empirical risk represents a minimum penalty if non-asymptotic guarantees concerning prediction error are to be provided. From the perspective of empirical process theory, this paper describes a concentration inequality for the supremum of a bounded non-centered (actually non-positive) empirical process. Combining the now classical analysis of M-estimation (building on Talagrand??s inequality for suprema of empirical processes) and versatile moment inequalities for functions of independent random variables, this paper develops a genuine Bernstein-like inequality that seems beyond the reach of traditional tools.     

再论中国股票市场的弱有效性

本文利用检验鞅差序列的非参数统计量来检验中国股票市场的弱有效性问题,本文给出的方法不同于文献中已有的方法,实证分析表明本文使用的检验股票市场弱有效的方法比文献中的大多数方法更有效。实证结果显示,中国的股市在发展过程中,表现山渐进有效的态势;中国A股市场比B股市场更有效率:沪市比深市更趋于有效。文中的有些结论是以前的实证研究所没有的。另外,本文比较了中国股市和境外成熟股市之间有效性程度的差异,并得出香港市场对深市影响显著的结论。     

On an aggregation model with long and short range interactions

In recent papers the authors had proposed a stochastic model for swarm aggregation, based on individuals subject to long range attraction and short range repulsion, in addition to a classical Brownian random dispersal. Under suitable laws of large numbers they showed that, for a large number of individuals, the evolution of the empirical distribution of the population can be expressed in terms of an approximating nonlinear degenerate and nonlocal parabolic equation, which describes the limit.In this paper the well-posedness of such evolution equation is investigated, which invokes a notion of entropy solutions extended to the nonlocal case. We motivate entropy solutions from the discrete particle system and use them to prove uniqueness. Moreover, we provide existence results and discuss some basic properties of solutions. Finally, we apply a Lagrangian numerical scheme to perform numerical simulations in spatial dimension one.     

Estimation of Lévy Processes via Stochastic Programming and Kalman Filtering

The estimation of Lévy process has received a lot of attention in recent years. Evidence of this is the extensive amount of literature concerning this problem which can be classified in two categories: the nonparametric approach, and the parametric approach. In this paper, we shall concentrate on the latter, and in particular the parameters will be estimated within a stochastic programming framework. To be more specific, the first derivative of the characteristic function and its empirical version shall be used in objective function. Furthermore, the parameter estimates are recursively estimated by making use of a modified extended Kalman filter (MEKF). Some properties of the parameter estimates are studied. Finally, a number of simulations will be carried out and the results are presented and discussed.     

A penalized criterion for variable selection in classification

In this paper, the problem of variable selection in classification is considered. On the basis of recent developments in model selection theory, we provide a criterion based on penalized empirical risk, where the penalization explicitly takes into account the number of variables of the considered models. Moreover, we give an oracle-type inequality that non-asymptotically guarantees the performance of the resulting classification rule. We discuss the optimality of the proposed criterion and present an application of the main result to backward and forward selection procedures.     

Risk bounds for model selection via penalization

Performance bounds for criteria for model selection are developed using recent theory for sieves. The model selection criteria are based on an empirical loss or contrast function with an added penalty term motivated by empirical process theory and roughly proportional to the number of parameters needed to describe the model divided by the number of observations. Most of our examples involve density or regression estimation settings and we focus on the problem of estimating the unknown density or regression function. We show that the quadratic risk of the minimum penalized empirical contrast estimator is bounded by an index of the accuracy of the sieve. This accuracy index quantifies the trade-off among the candidate models between the approximation error and parameter dimension relative to sample size. If we choose a list of models which exhibit good approximation properties with respect to different classes of smoothness, the estimator can be simultaneously minimax rate optimal in each of those classes. This is what is usually called adaptation. The type of classes of smoothness in which one gets adaptation depends heavily on the list of models. If too many models are involved in order to get accurate approximation of many wide classes of functions simultaneously, it may happen that the estimator is only approximately adaptive (typically up to a slowly varying function of the sample size). We shall provide various illustrations of our method such as penalized maximum likelihood, projection or least squares estimation. The models will involve commonly used finite dimensional expansions such as piecewise polynomials with fixed or variable knots, trigonometric polynomials, wavelets, neural nets and related nonlinear expansions defined by superposition of ridge functions. Received: 7 July 1995 / Revised version: 1 November 1997     

网络直播对餐饮O2O外卖顾客信任的影响研究

网络直播是基于互联网快速发展的一种实时视讯方式,其内容真实不可篡改,具有良好的实证作用。餐饮O2O外卖是以互联网为媒介,顾客线上下单,享受线下送餐上门的一种餐饮模式。但近几年外卖食品安全问题频发,导致顾客信任下降。基于网络直播的属性,本文构建了网络直播对餐饮O2O外卖顾客信任影响的结构方程模型。并通过问卷调查,进行了实证研究。研究结果表明网络直播的内容、主体、方式、时间、次数均对顾客信任具有显著性正向影响。最后根据研究结果提出了提高餐饮O2O外卖顾客信任及发展网络直播模式的建议。     

Choices and intervals

We consider a random interval splitting process, in which the splitting rule depends on the empirical distribution of interval lengths. We show that this empirical distribution converges to a limit almost surely as the number of intervals goes to infinity. We give a characterization of this limit as a solution of an ODE and use this to derive precise tail estimates. The convergence is established by showing that the size-biased empirical distribution evolves in the limit according to a certain deterministic evolution equation. Although this equation involves a non-local, non-linear operator, it can be studied thanks to a carefully chosen norm with respect to which this operator is contractive.     

Trading volume in models of financial derivatives

This paper develops a subordinated stochastic process model for an asset price, where the directing process is identified as information. Motivated by recent empirical and theoretical work, the paper makes use of the under-used market statistic of transaction count as a suitable proxy for the information flow. An option pricing formula is derived, and comparisons with stochastic volatility models are drawn. Both the asset price and the number of trades are used in parameter estimation. The underlying process is found to be fast mean reverting, and this is exploited to perform an asymptotic expansion. The implied volatility skew is then used to calibrate the model.     

Komlos-Major-Tusnady approximation for the general empirical process and Haar expansions of classes of functions

Rates of convergence of the Komlos-Major-Tusnady type in the invariance principle for empirical processes indexed by functions are obtained. The conditions are given in terms of the empirical entropy and the accuracy of the Haar type approximation for the corresponding classes of functions. The recent results of Massart⁽²⁵⁾ as well as some new results for empirical characteristic functions are obtained as a corollary.     

Diffusing particles with electrostatic repulsion

Summary. We study a diffusion model of an interacting particles system with general drift and diffusion coefficients, and electrostatic inter-particles repulsion. More precisely, the finite particle system is shown to be well defined thanks to recent results on multivalued stochastic differential equations (see [2]), and then we consider the behaviour of this system when the number of particles goes to infinity (through the empirical measure process). In the particular case of affine drift and constant diffusion coefficient, we prove that a limiting measure-valued process exists and is the unique solution of a deterministic PDE. Our treatment of the convergence problem (as ) is partly similar to that of T. Chan [3] and L.C.G. Rogers - Z. Shi [5], except we consider here a more general case allowing collisions between particles, which leads to a second-order limiting PDE. Received: 5 August 1996 / In revised form: 17 October 1996     

Stochastic dominance efficiency analysis of diversified portfolios: classification, comparison and refinements

For more than three decades, empirical analysis of stochastic dominance was restricted to settings with mutually exclusive choice alternatives. In recent years, a number of methods for testing efficiency of diversified portfolios have emerged, which can be classified into three main categories: (1)?majorization, (2)?revealed preference and (3)?distribution-based approaches. Unfortunately, some of these schools of thought are developing independently, with little interaction or cross-referencing among them. Moreover, the methods differ in terms of their objectives, the information content of the results and their computational complexity. As a result, the relative merits of alternative approaches are difficult to compare. This paper presents the first systematic review of all three approaches in a unified methodological framework. We examine the main developments in this emerging literature, critically evaluating the advantages and disadvantages of the alternative approaches. We also point out some misleading arguments and propose corrections and improvements to some of the methods considered.     

Recent applications of theory of the firm under uncertainty

This paper provides a comprehensive synthesis and analysis of the current and recent empirical implementations of the theory of the firm under uncertainty, including multiple sources of uncertainty. In so doing, it identifies the major estimation obstacles and it offers the future empirical researcher ways to derive simple estimation procedures under multiple sources uncertainty.     

A bivariate duration model for job mobility of two-earner households

In recent years we have observed an increasing interest in the job mobility patterns of employed persons. A new issue in this context is the mutual dependence of job mobility choice of workers who belong to the same household. In the present paper, we focus on bivariate duration models of stock sampled data in which dependence is induced through mixing. We apply the estimation method on the basis of an empirical study on the mutual dependence of job mobility of workers belonging to a two-earner household in the Netherlands. An interesting empirical result is that the marginal willingness to pay for a reduction in commuting time in a two-wage earner household is higher than that usually found for single-earner workers.     

平衡增加的经验欧氏似然

经验(欧氏)似然方法是近年来非常流行的一种非参数统计方法.针对经验(欧氏)似然的凸包限制和计算复杂问题,本文借助Emerson和Owen (2009)所提出的平衡增加思想对经验欧氏似然进行修正,得到了平衡增加的经验欧氏似然.随后论文从理论和模拟两个方面进行了研究.理论上给出了该方法与经验欧氏似然检验函数之间的联系,即在固定的样本量n下随着添加点位置的连续变化,检验方法可以从简单的均值增加经验欧氏似然变化到经验欧氏似然检验;模拟结果显示,适当选取调整因子,平衡增加的经验欧氏似然相对于(调整)经验欧氏似然而言,在大多数情况下,其分布更接近于对应的极限分布.     

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The additive model is a more flexible nonparametric statistical model which allows a data-analytic transform of the covariates.When the number of covariates is big and grows exponentially with the sample size the urgent issue is to reduce dimensionality from high to a moderate scale. In this paper, we propose and investigate marginal empirical likelihood screening methods in ultra-high dimensional additive models. The proposed nonparametric screening method selects variables by ranking a measure of the marginal empirical likelihood ratio evaluated at zero to differentiate contributions of each covariate given to a response variable. We show that, under some mild technical conditions, the proposed marginal empirical likelihood screening methods have a sure screening property and the extent to which the dimensionality can be reduced is also explicitly quantified. We also propose a data-driven thresholding and an iterative marginal empirical likelihood methods to enhance the finite sample performance for fitting sparse additive models. Simulation results and real data analysis demonstrate the proposed methods work competitively and performs better than competitive methods in error of a heteroscedastic case.     

Yield management in UK hotels: a systems analysis

A yield management (YM) system is one of the most recent examples of systems development in the hotel industry. YM systems interact with global distribution systems, property management systems and front office systems. However, despite the prevalence of the term ‘systems’, little operational research or systems analysis has been conducted in the hotel sector. In hospitality research journals, yield management is the most researched aspect of hotel operations, but such research is largely conceptual, with limited examples of empirical or systems analysis. This study was designed to undertake, for the first time, a soft systems analysis of yield management in hotels and to develop a systems model. Research was conducted in a number of hotels implementing yield management. One of the case studies is presented to illustrate this. Using this conceptual model, YM theory is critiqued, YM practice is discussed, and subsequently a number of YM issues are identified.     

我国上市公司审计收费影响因素的实证分析

本文首次选取我国第一支统一指数沪深300的指标股2005年年报披露的审计费用作为研究对象,应用多元线性回归的统计方法,对我国上市公司审计收费影响因素进行实证研究。研究表明:企业资产规模、纳入合并报表的子公司数、会计师事务所为"国际四大"显著影响审计收费;而存货和应收账款占总资产的比例、资产负债率、净资产收益率、审计意见类型、独立董事的数量、公司所在地等对审计收费不存在显著的影响。最后提出有针对性的政策建议。