Estimation in shape-constrained statistical models dates back at least as far as Hildreth [(1954); J. Amer. Statist. Assoc., 49, 598–619], who considered the maximum likelihood estimation of production functions under the natural assumption of non-increasing returns (which implies that the production function is concave and nondecreasing). Around the same time, Grenander [(1956); Skand. Aktuarietidskr., 39, 125–153], motivated by the theory of mortality measurement, studied the nonparametric maximum likelihood estimator of a decreasing density function on the positive half line. Over subsequent years, these ideas have been extended and developed in many different directions.

Shape-constrained methods may be applied to regression function estimation as well as density estimation, and allow the user to implement vague and qualitative assumptions about functional forms, without having to specify parametric models. This is extremely useful in the common situation where the only valid assumptions involve shape (and smoothness). Being nonparametric, the methods are more robust than standard parametric approaches. Further, although these methods deal with infinite- dimensional models (e.g., functions), estimation can still be carried out using the method of maximum likelihood (probably the main technique for estimation of parameters in a statistical model).

Conference Website

The annual general research conference on the Japanese economy will be held July 31, 2017 in Tokyo. The conference will be jointly sponsored by the NBER, Australia-Japan Research Centre, Center for Advanced Research in Finance, and the Center for Japanese Economy and Business. Last years conference was highlighted by papers by Robert Delke and Hajime Tomura with outstanding discussants such as Nobuhiro Kiyotaki and Stephen Redding.

Economic and Social Research Institute (ESRI) will hold their annual research conference on August 1st, 2017 following the NBER Japan project meeting.

The Economic and Social Research Institute (ESRI) was founded in January 2001 as part of the Cabinet Office with the expansion of the functions and scale of the Economic Planning Agency’s Economic Research Institute under the reorganization of Japan’s central government ministries and agencies. Known as the “forum for knowledge” for the Cabinet, the Cabinet Office is in charge of key administrative issues. As the Cabinet Office’s think tank, the ESRI could rightly be called the “forum for knowledge” within the “forum for knowledge” because the ESRI links theory with policy.

The main role of the ESRI is to strengthen its functions as a policy research institute by conducting theoretical and experimental research related to economic activities and policies, social activities, and other issues. The ESRI also trains and develops human resources to spearhead policy research. In addition, the ESRI compiles and publishes estimates for the gross domestic product (GDP) and other statistics in the System of National Accounts (SNA). The ESRI publishes both the Preliminary Quarterly GDP Estimates and the Annual Report on National Accounts. Moreover, the ESRI compiles and publishes the Coincident Index and other business statistics.

EWEPA is a major biennial conference on the topics of productivity, efficiency and performance analysis. We expect a very large number of participants to attend, from a wide international community. Presentations are invited on the theory and application of economics, econometrics, statistics, management science and operational research to various problems in the areas of productivity and efficiency. All popular techniques and methodologies will be represented, including stochastic frontier analysis, data envelopment analysis, bootstrapping approaches, and many more. We also welcome papers on broader issues related to measuring, understanding, incentivising and improving the productivity and performance of firms, public services and industries.

The INFORMS 2016 Annual Meeting will take place in the Music City Center, the state-of-the-art convention center recently built, and the adjacent Omni Nashville Hotel. The conference will feature plenaries and keynotes; panel discussions; tutorials; and thousands of oral and poster presentations from leading academics, industry experts, students, and representatives of government agencies. This Annual Meeting will provide the attendees numerous opportunities to share expertise and experiences; find prospective employers and employees; and build professional networks by meeting new people and reconnecting with colleagues.

The College-Industry Council on Material Handling Education (CICMHE) held its bi-annual business meeting October 15th and 16th in Tucson, Arizona. CICHME is an academic/industry council of the Material Handling Industry (MHI) that promotes increased awareness of material handling and logistics through a variety of educational and research activities. The council is organized into two working committees: Events and Projects.

The Conference on Advances in Big Data Modeling, Computation and Analytics was held at Texas A&M University on September 23 and 24 featuring a variety of academic and industry talks. A highlight of the event was a keynote talk by Michael Jordan, the abstract is included below.

On Computational Thinking, Inferential Thinking and Data Science
Michael I. Jordan, University of California, Berkeley

ABSTRACT
The rapid growth in the size and scope of datasets in science and technology has created a need for novel foundational perspectives on data analysis that blend the inferential and computational sciences. That classical perspectives from these fields are not adequate to address emerging problems in “Big Data” is apparent from their sharply divergent nature at an elementary level—in computer science, the growth of the number of data points is a source of “complexity” that must be tamed via algorithms or hardware, whereas in statistics, the growth of the number of data points is a source of “simplicity” in that inferences are generally stronger and asymptotic results can be invoked. On a formal level, the gap is made evident by the lack of a role for computational concepts such as “runtime” in core statistical theory and the lack of a role for statistical concepts such as “risk” in core computational theory. I present several research vignettes aimed at bridging computation and statistics, including the problem of inference under privacy and communication constraints, and ways to exploit parallelism so as to trade off the speed and accuracy of inference.

The Texas Research Data Center (RDC) hostied the 2016 FSRDC Research Conference on September 15, 2016. The Texas RDC is supported by a multi-university consortium involving Texas A&M University, Texas A&M University System, Baylor University, Rice University, The University of Texas at Austin and The University of Texas at San Antonio. The conference was a day of concurrent paper and poster sessions and a keynote presentation. Sessions were based on current or recent research using data from the nationwide network of RDCs. Themes include, research topics from the fields of economics, business and management, demography, and health and developments in data sets. Papers and posters involved statistical analyses on nonpublic versions of data sets available from the U.S. Census Bureau, the National Center for Health Statistics, the Agency for Healthcare Research and Quality, and other federal statistical agencies.