New ASA Fellows—1967

This paper presents at an elementary level a unified presentation of concepts related to sufficiency and minimal sufficiency. Extensively discussed are techniques for showing in a particular statistical model that a given statistic is not sufficient or that a given sufficient statistic is not minimal. The applicability of these techniques is illustrated in three examples.     

New ASA Fellows — 1964

This is an invited expository article for The American Statistician. It reviews the nonparametric estimation of statistical error, mainly the bias and standard error of an estimator, or the error rate of a prediction rule. The presentation is written at a relaxed mathematical level, omitting most proofs, regularity conditions, and technical details.     

Profile—Educational Attainments of ASA Members, 1964

The lognormal distribution is useful for approximating the distributions of such input variables as costs, sales, market share, etc. required in Monte Carlo simulations of business decisions. An easy way to represent such distributions is by the tenth and ninetieth percentiles and the mode. Such representation, however, does not permit direct calculation of the usual characterizing parameters (θ, γ, and δ). An empirically-determined rational function is given by which the median can be approximated from the mode, which then permits direct calculation of θ, γ, and δ.     

Forecasting the Penetration of a New Product—A Bayesian Approach

We adopt a Bayesian approach to forecast the penetration of a new product into a market. We incorporate prior information from an existing product and/or management judgments into the data analysis. The penetration curve is assumed to be a nondecreasing function of time and may be under shape constraints. Markov-chain Monte Carlo methods are proposed and used to compute the Bayesian forecasts. An example on forecasting the penetration of color television using the information from black-and-white television is provided. The models considered can also be used to address the general bioassay and reliability stress-testing problems.     

New Developments in Statistical Computing: New Bias Evaluation Features of EXPLOR—A Program for Assessing Experimental Design Properties

Longitudinal investigations play an increasingly prominent role in biomedical research. Much of the literature on specifying and fitting linear models for serial measurements uses methods based on the standard multivariate linear model. This article proposes a more flexible approach that permits specification of the expected response as an arbitrary linear function of fixed and time-varying covariates so that mean-value functions can be derived from subject matter considerations rather than methodological constraints. Three families of models for the covariance function are discussed: multivariate, autoregressive, and random effects. Illustrations demonstrate the flexibility and utility of the proposed approach to longitudinal analysis.     

Who Will Celebrate Our 200th Anniversary? Growing the Next Generation of ASA Members

During the next 25 years, the growth and vitality of the American Statistical Association will depend on how well we attract and serve members in emerging areas of practice such as data science, where statistics as a skill set is in high demand but statistics as a profession has low recognition. Successful adaptation to the era of Big Data requires that we broaden our understanding of statistical practice to include the work of all those who learn from data. In order to grow the next generation of members, we must also retain a much higher proportion of today's student members, many of whom leave the ASA upon graduation. By providing value that meets the needs of these groups and equips them to flourish in their organizations, we can become the Big Tent for Statistics.     

两份纲要 一个主题——关于ASA两份统计学评估与教育指导纲要的思考

针对目前中国统计学界普遍关心的教材更新、教学互动、评估手段等问题,结合美国统计协会(ASA)2005年发布的"统计学评估和教育指导纲要"(Guidelines for Assessment and Instruction in Statistics Education,以下简称GAISE)及其姊妹篇(Guidelines for Assessment and Instruction in Statistics Education:A Pre-K-12Curriculum Framework,以下简称GAISE Pre-K-12),采用对比分析方法,深入地阐述了这两份纲要对近年来美国统计教育水平不断提高所发挥的重要作用,并在此基础上探讨了它们对中国统计教育改革可能带来的启发。研究认为:中国中小学、大学统计教育改革必须联动进行;统计学课本编纂应突出"提出问题—收集数据—分析数据—解释数据"这一教学主线;计算机辅助教学水平及互联网在线支持力度应持续提高,以培养具有坚实统计学知识及统计思维能力的一代新人。     

Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970–1996

In this article we present a technique for implementing large-scale optimal portfolio selection. We use high-frequency daily data to capture valuable statistical information in asset returns. We describe several statistical issues involved in quantitative approaches to portfolio selection. Our methodology applies to large-scale portfolio-selection problems in which the number of possible holdings is large relative to the estimation period provided by historical data. We illustrate our approach on an equity database that consists of stocks from the Standard and Poor's index, and we compare our portfolios to this benchmark index. Our methodology differs from the usual quadratic programming approach to portfolio selection in three ways: (1) We employ informative priors on the expected returns and variance-covariance matrices, (2) we use daily data for estimation purposes, with upper and lower holding limits for individual securities, and (3) we use a dynamic asset-allocation approach that is based on reestimating and then rebalancing the portfolio weights on a prespecified time window. The key inputs to the optimization process are the predictive distributions of expected returns and the predictive variance-covariance matrix. We describe the statistical issues involved in modeling these inputs for high-dimensional portfolio problems in which our data frequency is daily. In our application, we find that our optimal portfolio outperforms the underlying benchmark.