Statistical Inference,Learning and Models in Big Data

The need for new methods to deal with big data is a common theme in most scientific fields, although its definition tends to vary with the context. Statistical ideas are an essential part of this, and as a partial response, a thematic program on statistical inference, learning and models in big data was held in 2015 in Canada, under the general direction of the Canadian Statistical Sciences Institute, with major funding from, and most activities located at, the Fields Institute for Research in Mathematical Sciences. This paper gives an overview of the topics covered, describing challenges and strategies that seem common to many different areas of application and including some examples of applications to make these challenges and strategies more concrete.     

Budgetary Models,Motivation and Engagement in Financial Collaborations

Abstract

This article focuses on the financial aspects of collaborative working between public authorities and other private and voluntary sector organizations. Drawing on research on five English case studies involving local authorities, it considers the extent to which decisions to collaborate and modes of working together are shaped and mediated by financial considerations. In particular, the research highlights differences in ‘budgetary form’ (pooled or aligned), individual partner ‘motivation’ (selfless/altruistic or self-interested) and ‘level of engagement’ (active commitment or passive involvement) as key variables that help to understand how and why some financial collaborations are more successful than others.     

Marginalized Predictive Likelihood Comparisons of Linear Gaussian State‐Space Models with Applications to DSGE,DSGE‐VAR,and VAR Models

The predictive likelihood is useful for ranking models in forecast comparison exercises using Bayesian inference. We discuss how it can be estimated, by means of marzginalization, for any subset of the observables in linear Gaussian state‐space models. We compare macroeconomic density forecasts for the euro area of a DSGE model to those of a DSGE‐VAR, a BVAR and a multivariate random walk over 1999:Q1–2011:Q4. While the BVAR generally provides superior forecasts, its performance deteriorates substantially with the onset of the Great Recession. This is particularly notable for longer‐horizon real GDP forecasts, where the DSGE and DSGE‐VAR models perform better. Copyright © 2016 John Wiley & Sons, Ltd.     

两类DSGE模型的动态因子模型表示

为了识别动态因子模型（DFM）中不可观测动态因子、并解读其经济学意义,以及为DSGE模型提供一种新的估计途径,研究了新凯恩斯DSGE模型和具有Markov体制转换过程的DSGE（MS-DSGE）模型的DFM表示。结果表明,DSGE模型和MS-DSGE模型的动态行为均由共同因子和外生冲击的合并效应所决定;当外生冲击为相互独立的白噪声过程时,两类DSGE模型分别可以表示为标准的DFM和具有Markov体制转换过程的DFM。     

A Tutorial on Multilevel Survival Analysis: Methods,Models and Applications

Data that have a multilevel structure occur frequently across a range of disciplines, including epidemiology, health services research, public health, education and sociology. We describe three families of regression models for the analysis of multilevel survival data. First, Cox proportional hazards models with mixed effects incorporate cluster‐specific random effects that modify the baseline hazard function. Second, piecewise exponential survival models partition the duration of follow‐up into mutually exclusive intervals and fit a model that assumes that the hazard function is constant within each interval. This is equivalent to a Poisson regression model that incorporates the duration of exposure within each interval. By incorporating cluster‐specific random effects, generalised linear mixed models can be used to analyse these data. Third, after partitioning the duration of follow‐up into mutually exclusive intervals, one can use discrete time survival models that use a complementary log–log generalised linear model to model the occurrence of the outcome of interest within each interval. Random effects can be incorporated to account for within‐cluster homogeneity in outcomes. We illustrate the application of these methods using data consisting of patients hospitalised with a heart attack. We illustrate the application of these methods using three statistical programming languages (R, SAS and Stata).     

RAM: A Range Adjusted Measure of Inefficiency for Use with Additive Models,and Relations to Other Models and Measures in DEA

Generalized Efficiency Measures (GEMS) for use in DEA are developed and analyzed in a context of differing models where they might be employed. The additive model of DEA is accorded a central role and developed in association with a new measure of efficiency referred to as RAM (Range Adjusted Measure). The need for separately treating input oriented and output oriented approaches to efficient measurement is eliminated because additive models effect their evaluations by maximizing distance from the efficient frontier (in ₁, or weighted ₁, measure) and thereby simultaneously maximize outputs and minimize inputs. Contacts with other models and approaches are maintained with theorems and accompanying proofs to ensure the validity of the thus identified relations. New criteria are supplied, both managerial and mathematical, for evaluating proposed measures. The concept of approximating models is used to further extend these possibilities. The focus of the paper is on the physical aspects of performance involved in technical and mix inefficiencies. However, an Appendix shows how overall, allocative and technical inefficiencies may be incorporated in additive models.     

Multicointegration in Stock-Flow Models

Multicointegration, in the sense of Granger and Lee (1990), frequently occurs in models of stock-flow adjustment and implies cointegration amongst I(2) variables and their differences (polynomial cointegration). The purpose of this article is two-fold. First, we demonstrate that based on a multicointegrated vector autoregression (VAR) two equivalent error correction model (ECM) representations can be derived; the first is expressed in terms of adjustments in the flows of the variables (the standard I(2) ECM), and the second is expressed in terms of adjustments in both the stocks and the flows. Secondly, we apply I(2) estimation and testing procedures for multicointegrated time series to analyze data for US housing construction. We find that stocks of housing units started and completed exhibit poly- nomial cointegration (and hence the flows are multicointegrated) and the associated ECM's are estimated. Lee (1992, 1996) also found multicointegration in this data set but without explicitly exploiting the I(2) property.     

Models of military expenditure

A brief review of the extensive empirical literature devoted to explaining levels of military expenditure indicates a wide variety of unreconciled results. However, comparing the alternative models is not straightforward. This paper uses British post-war military expenditures to illustrate some of the methodological issues involved in model comparison, evaluation and selection. Starting from two published models for the UK, a systematic specification search leads to a new model, which dominates alternative specifications, passes a wide range of misspecification tests, and also fits well to French data.     

指数自回归条件异方差模型的检验

本文在达尼艾尔松(1994)定义的随机波动模型(SV)的基础上,借用迪拉克.德鲁塔函数的思想,提出检验指数自回归条件异方差(EGARCH)模型的拉格朗日乘数检验统计量。该检验统计量的提出预示着一定条件下EGARCH模型为SV模型的一种特例。最后通过蒙特卡罗计算机仿真实验验证该检验统计量的检验能力。     

Models of stratification processes

Quality &; Quantity - This paper presents certain mathematical models of the stratification process. These models are explicity concerned with the dynamics of stratification systems, and through...     

Models for truncated counts

This paper examines a class of maximum-likelihood regression estimators for count data from truncated samples. Estimators for the truncated Poisson and negative binomial distributions are illustrated. Simulation results are given to illustrate the magnitude of the bias that may result from the failure to account for overdispersion in truncated samples. An empirical application based upon the number of recreational fishing trips taken by a sample of Alaskan fishermen is provided.     

构建企业会计模式

企业会计模式是国家会计模式在企业的具体体现。与一个国家相比,企业所处环境相对有限,因此,有些构建国家会计模式所涉及的要素在企业会计模式中就成为理所当然的前提或基础,而不需要作为企业构建会计模式时考虑的问题。