A Survey of the Teaching of Statistics in M.B.A. Programs

A survey of business schools was conducted to obtain information about the current state of the teaching of business statistics to students enrolled in M.B.A. degree programs. The survey was undertaken for and presented at a June 1986 conference on “Making Statistics More Effective in Schools of Business,” held at the University of Chicago's Graduate School of Business. Information was elicited concerning both the required statistics sequence and elective statistics courses for M.B.A. students, as well as computer usage in these courses. This article summarizes the information obtained from the survey.     

Parametric Nonlinear Regression with Endogenous Switching

Based on the insightful work of Olsen (1980 Olsen , R. J. ( 1980 ). A least squares correction for selectivity bias . Econometrica 48 : 1815 – 1820 .[Crossref], [Web of Science ®] , [Google Scholar]) for the linear context, a generic and unifying framework is developed that affords a simple extension of the classical method of Heckman (1974 Heckman , J. ( 1974 ). Shadow prices, market wages, and labor supply . Econometrica 42 : 679 – 694 .[Crossref], [Web of Science ®] , [Google Scholar], 1976 Heckman , J. ( 1976 ). The common structure of statistical models of truncation sample selection and limited dependent variables and a simple estimator for such models . Annals of Economic and Social Measurement 5 : 475 – 492 . [Google Scholar], 1978 Heckman , J. ( 1978 ). Dummy endogenous variables in a simultaneous equation system . Econometrica 46 : 931 – 959 .[Crossref], [Web of Science ®] , [Google Scholar], 1979 Heckman , J. ( 1979 ). Sample selection bias as a specification error . Econometrica 47 : 153 – 161 .[Crossref], [Web of Science ®] , [Google Scholar]) to a broad class of nonlinear regression models involving endogenous switching and its two most common incarnations, endogenous sample selection and endogenous treatment effects. The approach should be appealing to applied researchers for three reasons. First, econometric applications involving endogenous switching abound. Secondly, the approach requires neither linearity of the regression function nor full parametric specification of the model. It can, in fact, be applied under the minimal parametric assumptions—i.e., specification of only the conditional means of the outcome and switching variables. Finally, it is amenable to relatively straightforward estimation methods. Examples of applications of the method are discussed.     

Developing “A Framework for Planning U.S. Federal Statistics, 1978–1989”

The Statistical Policy Division of the Office of Management and Budget has the overall responsibility for the planning and coordination of U.S. government statistics. The present staff of the Statistical Policy Division is attempting, through an integrated publication entitled “A Framework for Planning U.S. Federal Statistics, 1978–1989,” to state its perspective on necessary developments in the coming years. This article illustrates the character of the Framework materials and outlines the process for public review and comment on this undertaking.     

On constructing T 2 control charts for retrospective examination

In this paper we consider the issue of constructing retrospective T ² control chart limits so as to control the overall probability of a false alarm at a specified value. We describe an exact method for constructing the control limits for retrospective examination. We then consider Bonferroni-adjustments to Alt's control limit and to the standard x ² control limit as alternatives to the exact limit since it is computationally cumbersome to find the exact limit. We present the results of some simulation experiments that are carried out to compare the performance of these control limits. The results indicate that the Bonferroni-adjusted Alt's control limit performs better that the Bonferroni-adjusted x ² control limit. Furthermore, it appears that the Bonferroni-adjusted Alt's control limit is more than adequate for controlling the overall false alarm probability at a specified value.     

Functional principal component analysis for the explorative analysis of multisite–multivariate air pollution time series with long gaps

The knowledge of the urban air quality represents the first step to face air pollution issues. For the last decades many cities can rely on a network of monitoring stations recording concentration values for the main pollutants. This paper focuses on functional principal component analysis (FPCA) to investigate multiple pollutant datasets measured over time at multiple sites within a given urban area. Our purpose is to extend what has been proposed in the literature to data that are multisite and multivariate at the same time. The approach results to be effective to highlight some relevant statistical features of the time series, giving the opportunity to identify significant pollutants and to know the evolution of their variability along time. The paper also deals with missing value issue. As it is known, very long gap sequences can often occur in air quality datasets, due to long time failures not easily solvable or to data coming from a mobile monitoring station. In the considered dataset, large and continuous gaps are imputed by empirical orthogonal function procedure, after denoising raw data by functional data analysis and before performing FPCA, in order to further improve the reconstruction.     

Statistical Certification of Software Systems

Common software release procedures based on statistical techniques try to optimize the trade-off between further testing costs and costs due to remaining errors. We propose new software release procedures where the aim is to certify with a certain confidence level that the software does not contain errors. The underlying model is a discrete time model similar to the geometric Moranda model. The decisions are based on a mix of classical and Bayesian approaches to sequential testing and do not require any assumption on the initial number of errors.     

Meta-cognition and resistance to political persuasion: evidence from a three-wave panel study

ABSTRACT

We investigate the temporal course of meta-cognition and resistance processes following exposure to counter-attitudinal information in the 2012 Presidential election. Using a unique 3-wave survey panel design, we tracked eligible voters during the last months of the 2012 campaign and experimentally manipulated exposure to negative political messages targeting Barack Obama and Mitt Romney on an online platform. As predicted, we found that politically unengaged (vs. engaged) individuals were less likely to counter-argue a message attacking their favored candidate. Resistance, in turn, led to increased attitudinal certainty, polarization, and correspondence with actual voting behavior over the course of the campaign. These findings provide the first analysis of the longitudinal effects of meta-cognitive processes underlying persuasion for real-world attitude change and behavior.     

Twitter,incivility, and presidential communication: A theoretical incursion into spectacle and power

ABSTRACT

By approaching civility as an operational logic for democracy, we ask how incivility is a strategy related to power and domination, particularly from the president of the United States. We propose that Twitter is part of an infrastructure of incivility, through which structures and discursive mechanisms contribute to a devaluation of normative democratic discourses. Spectacle provides a theoretical framework to contextualize the forces at play in mediating our relations. Using President Trump’s Twitter use as a case study, we offer four propositions that together formulate a framework for theorizing the strategic use and deployment of incivility as an increasingly legitimate, yet problematic tool for democratic governance.     

金融法重构之社会基础:金融利益关系统合化运动

金融利益关系的运动轨迹是金融法重构的基础。通过分析金融利益关系重整、系统性风险、次贷危机三者"前推后"的因果关系脉络,以及"重整"的具体安排、变动过程,推知金融利益关系运动规律与统合化发展趋势。并通过分析"重整"的产生原因,得出金融创新与金融法约束的应然适度关系。基于以上规律、趋势与关系的总结,提出金融法重构的框架思路。     

Profiles of Housing Cost Burden in the United States

In this study, the ordered logistic regression model indicates that renters suffer a greater housing cost burden than homeowners and that the lower the income of the household, the larger the portion of income that is spent for housing. Further, the model also shows that the burden of high housing costs falls disproportionately on certain groups of American households. Compared to the reference group, two single-risk groups (Asian-American households and households with three or more children) and two dual-risk groups (female-headed households with three or more children and minority households with three or more children) tend to have a higher risk of excessive housing costs. Elderly households and three elderly-related groups (female-headed elderly households, minority elderly households, and female-headed minority elderly households), however, tend to have a lower risk of housing cost burden than other households. The housing cost burden of the reference group is comparable to the housing cost burden of each of the following groups: for single female-headed households; Black households; Hispanic households; and female-headed minority households. The policy implications of these findings are discussed in the paper.