4,963 research outputs found
On the future of astrostatistics: statistical foundations and statistical practice
This paper summarizes a presentation for a panel discussion on "The Future of
Astrostatistics" held at the Statistical Challenges in Modern Astronomy V
conference at Pennsylvania State University in June 2011. I argue that the
emerging needs of astrostatistics may both motivate and benefit from
fundamental developments in statistics. I highlight some recent work within
statistics on fundamental topics relevant to astrostatistical practice,
including the Bayesian/frequentist debate (and ideas for a synthesis),
multilevel models, and multiple testing. As an important direction for future
work in statistics, I emphasize that astronomers need a statistical framework
that explicitly supports unfolding chains of discovery, with acquisition,
cataloging, and modeling of data not seen as isolated tasks, but rather as
parts of an ongoing, integrated sequence of analyses, with information and
uncertainty propagating forward and backward through the chain. A prototypical
example is surveying of astronomical populations, where source detection,
demographic modeling, and the design of survey instruments and strategies all
interact.Comment: 8 pp, 2 figures. To appear in "Statistical Challenges in Modern
Astronomy V," (Lecture Notes in Statistics, Vol. 209), ed. Eric D. Feigelson
and G. Jogesh Babu; publication planned for Sep 2012; see
http://www.springer.com/statistics/book/978-1-4614-3519-
On nonparametric estimation of a mixing density via the predictive recursion algorithm
Nonparametric estimation of a mixing density based on observations from the
corresponding mixture is a challenging statistical problem. This paper surveys
the literature on a fast, recursive estimator based on the predictive recursion
algorithm. After introducing the algorithm and giving a few examples, I
summarize the available asymptotic convergence theory, describe an important
semiparametric extension, and highlight two interesting applications. I
conclude with a discussion of several recent developments in this area and some
open problems.Comment: 22 pages, 5 figures. Comments welcome at
https://www.researchers.one/article/2018-12-
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Econometrics: A bird's eye view
As a unified discipline, econometrics is still relatively young and has been transforming and expanding very rapidly over the past few decades. Major advances have taken place in the analysis of cross sectional data by means of semi-parametric and non-parametric techniques. Heterogeneity of economic relations across individuals, firms and industries is increasingly acknowledge and attempts have been made to take them into account either by integrating out their effects or by modeling the sources of heterogeneity when suitable panel data exists. The counterfactual considerations that underlie policy analysis and treatment evaluation have been given a more satisfactory foundation. New time series econometric techniques have been developed and employed extensively in the areas of macroeconometrics and finance. Non-linear econometric techniques are used increasingly in the analysis of cross section and time series observations. Applications of Bayesian techniques to econometric problems have been given new impetus largely thanks to advances in computer power and computational techniques. The use of Bayesian techniques have in turn provided the investigators with a unifying framework where the tasks and forecasting, decision making, model evaluation and learning can be considered as parts of the same interactive and iterative process; thus paving the way for establishing the foundation of the "real time econometrics". This paper attempts to provide an overview of some of these developments
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