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Plenary Talks CF5 Thursday, September 5th 15:30 ABC-CDE: Toward Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations Rafael Izbicki UFSCar Approximate Bayesian computation (ABC) is typically used when the likelihood is either unavailable or intractable but where data can be simulated under different parameter settings using a forward model. Despite the recent interest in ABC, high-dimensional data and costly simulations still remain a bottleneck in some applications. There is also no consensus as to how to best assess the performance of such methods without knowing the true posterior. We show how a nonparametric conditional density estimation (CDE) framework, which we refer to as ABC-CDE, help address three nontrivial challenges in ABC: (i) how to efficiently estimate the posterior distribution with limited simulations and different types of data, (ii) how to tune and compare the performance of ABC and related methods in estimating the posterior itself, rather than just certain properties of the density, and (iii) how to efficiently choose among a large set of summary statistics based on a CDE surrogate loss. We provide theoretical and empirical evidence that justify ABC-CDE procedures that directly estimate and assess the posterior based on an initial ABC sample, and we describe settings where standard ABC and regression-based approaches are inadequate. Supplemental materials for this article are available online. Keywords: Nonparametric methods; Conditional density estimation; Approximate Bayesian computation; Likelihood-free inference 12
Plenary Talks CF6 Thursday, September 5th 17:00 Stationary Count Time Series Robert Lund Clemson University This talk overviews the modeling of stationary count time series, detailing some history and recent breakthroughs. Classical work involving the discrete and integer autoregressive moving-average model classes is first reviewed. Drawbacks with these models are illuminated and used to motivate two more modern approaches: copulas and construction from stationary sequences of zeroes and ones. What emerges are very flexible model classes that are naturally parsimonious, can have negative autocorrelations and/or long-memory features, and can be statistically fitted by likelihood, composite likelihood, and/or moment methods. Various applications are pursued, including a bivariate hurricane count model with Poisson components that are negatively correlated. 13
Page 1 and 2: BOOK OF ABSTRACTS
Page 3 and 4: Contents About the 18th ESTE 4 Time
Page 5 and 6: Timetable The 18th ESTE will be occ
Page 7 and 8: Minicourse Wednesday to Friday, Sep
Page 9 and 10: Plenary Talks CF2 Wednesday, Septem
Page 11: Plenary Talks CF4 Thursday, Septemb
Page 15 and 16: Plenary Talks CF8 Friday, September
Page 17 and 18: Thematic Sessions ST1: Data Science
Page 19 and 20: ST2: Nonlinear Time Series Chair: A
Page 21 and 22: ST2: Nonlinear Time Series Chair: A
Page 23 and 24: ST3: High Dimensional Time Series S
Page 25 and 26: Thematic Sessions ST4: Econometrics
Page 27 and 28: Thematic Sessions ST4: Econometrics
Page 29 and 30: Oral Communications CO1 Tuesday, Se
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Useful Information Talks will be he
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