ISM Symposium on Environmental Statistics 2020
Because of the COVID19, we have decided to cancel the workshop (26, Feb. 2020).
 Dates
 2627 March, 2020
 Venue
 Auditorium @ The Institute of Statistical Mathematics
 Host Organization
 The Institute of Statistical Mathematics
 Main Subjects 〈Statistical methods supporting environmental statistics〉

・Spatial statistics
・Spacetime modeling
・Model selection
・Bayesian inference
・Markov Chain Monte Carlo
・Circular distributions
・etc.
 Organizers

Alan Welsh (The Australian National University, Australia)
Song Xi, Chen (Peking University, China)
Pierre Dutilleul (McGill University, Canada)
Shuhei Mano (The Institute of Statistical Mathematics)
Shogo Kato (The Institute of Statistical Mathematics)
Koji Kanefuji (The Institute of Statistical Mathematics)
Kunio Shimizu (The Institute of Statistical Mathematics)
 Invited Speakers
 Pierre R. L. Dutilleul (McGill University, Canada)
HsinCheng Huang (Institute of Statistical Science, Academia Sinica, Taiwan)
Shogo Kato (The Institute of Statistical Mathematics, Japan)
Yoshinori Kawasaki (The Institute of Statistical Mathematics, Japan)
Jaeyong Lee (Seoul National University, Korea)
Shuhei Mano (The Institute of Statistical Mathematics, Japan)
Daisuke Murakami (The Institute of Statistical Mathematics, Japan)
Shunichi Nomura (The Institute of Statistical Mathematics, Japan)
Janice Scealy (Australian National University, Australia)
Alan Welsh (Australian National University, Australia)
Program
26 March
10:0010:10  Opening Address Hiroe Tsubaki (DirectorGeneral, The Institute of Statistical Mathematics) 

Session 1 (10:1011:40)  Chairperson: Kenichiro Shimatani (The Institute of Statistical Mathematics) 
10:1011:00  Scaled von MisesFisher Distributions and Regression Models for Palaeomagnetic Directional Data Janice Scealy (Australian National University, Australia) We propose a new distribution for analysing palaeomagnetic directional data that is a novel transformation of the von MisesFisher distribution. The new distribution has ellipselike symmetry, as does the Kent distribution; however, unlike the Kent distribution the normalising constant in the new density is easy to compute and estimation of the shape parameters is straightforward. To accommodate outliers, the model also incorporates an additional shape parameter which controls the tailweight of the distribution. We also develop a general regression model framework that allows both the mean direction and the shape parameters of the error distribution to depend on covariates. To illustrate, we analyse palaeomagnetic directional data from the GEOMAGIA50.v3 database. We predict the mean direction at various geological time points and show that there is significant heteroscedasticity present. It is envisaged that the regression structures and error distribution proposed here will also prove useful when covariate information is available with (i) other types of directional response data; and (ii) squareroot transformed compositional data of general dimension. This is joint work with Andrew T. A. Wood. 
11:0011:40  A MixedEffects Model for Circular Data Shogo Kato (The Institute of Statistical Mathematics, Japan) Circular data, comprising of angular observations, arise in various disciplines including environmental sciences. In this talk, we propose a mixedeffects model in which responses are circular variables and covariates are multiple circular and linear variables. The random effects and random errors of the proposed model are assumed to have the von Mises distributions. A measure of intraclass circular correlation and a predictor for an unobserved random effect are studied. Preliminary estimators for the parameters of the model are presented, and their performance is compared with that of the maximum likelihood estimators in a simulation study. A numerical example investigating the factors impacting the orientation taken by a sand hopper when released is presented. This is joint work with LouisPaul Rivest of Universite Laval, Canada. 
11:4013:00  Lunch Break 
Session 2 (13:0014:30)  Chairperson: Shuhei Mano (The Institute of Statistical Mathematics) 
13:0013:50  MultiResolution Spatial Methods with Applications HsinCheng Huang (Institute of Statistical Science, Academia Sinica, Taiwan) In this talk, I will introduce a multiresolution spatial method, which uses a special class of basis functions extracted from thinplate splines. The functions are ordered in terms of their degrees of smoothness with higherorder functions corresponding to largerscale features and lowerorder ones corresponding to smallerscale details, leading to a parsimonious representation of a spatial process with the number of basis functions playing the role of spatial resolution. Some applications of this method will be introduced, including spatial prediction of fine particulate matter (PM2.5) based on data from small internetofthings sensing devices, called AirBoxes, and independence test between two spatial random fields using a dimensionreduced canonical correlation analysis method. 
13:5014:30  SpatioTemporal Renewal Model for Repeating Earthquakes to Estimate Interplate Slip Rate Shunichi Nomura, Yosihiko Ogata, Naoki Uchida and Mitsuhiro Matsu'ura (The Institute of Statistical Mathematics, Japan) Repeating earthquake sequences on the plate subduction zone represent the sliprate histories around their fault patches. So they are useful resources for monitoring precursory aseismic slip of major earthquakes on plate boundaries. Repeating earthquakes are often modeled by renewal processes, point processes whose recurrence intervals are independent and identically distributed. However, their repeating intervals are greatly influenced by larger seismic events or aseismic slow slip, and hence we need to model such nonstationary behavior of repeating earthquakes. In this study, we propose a nonstationary spacetime model for repeating earthquakes based on the renewal process. We use the empirical relation between magnitudes and slip sizes of repeating earthquakes to estimate the sliprate histories in repeating sequences. The proposed model can estimate spatiotemporal variation in slip rate with smoothness restriction adjusted to optimize its Bayesian likelihood. We apply the proposed model to the large catalog of repeating earthquakes on subduction zone of Pacific Plate in the northeastern Japan and estimate sliprate history of the plate boundary. From this analysis, we discuss the characteristic changes in slip rate before and after the major earthquakes in that area. 
14:3014:50  Coffee Break 
Session 3 (14:5017:50)  Chairperson: Kunio Shimizu (The Institute of Statistical Mathematics) 
14:5015:40  PostProcessed Posteriors for HighDimensional Covariances Kwangmin Lee (Seoul National University, Korea), Kyoungjae Lee (Inha University, Korea), and Jaeyong Lee (Seoul National University, Korea) We consider Bayesian inference of banded covariance matrices and propose a postprocessed posterior. The postprocessing of the posterior consists of two steps. In the first step, posterior samples are obtained from the conjugate inverseWishart posterior which does not satisfy any structural restrictions. In the second step, the posterior samples are transformed to satisfy the structural restriction through a postprocessing function. The conceptually straightforward procedure of the postprocessed posterior makes its computation efficient and can render interval estimators of any functional of covariance matrices. We also show that it has nearly optimal minimax rates for banded and bandable covariances among all possible pairs of priors and postprocessing functions. The advantages of the postprocessed posterior are demonstrated by a simulation study and a real data analysis. 
15:4016:20  Parameter Estimation of the Generalized Beta Distributions and Its Application to a Historical Tsunami Magnitude Dataset Shuhei Mano (The Institute of Statistical Mathematics, Japan) and Masaaki Sibuya (Keio University, Japan) For estimating the generalized (fourparameter) beta distributions, NagatsukaBalakrishnanKamakura transformation is applied to the shape parameter, combined with Hall and Wang's empirical Bayesian likelihood to the locationscale parameter. With this procedure and a smoothing, a new estimator of parameters is proposed. Some nonnormal limit distributions of the estimators of the locationscale parameter are discussed, and the performance of the proposed estimator is evaluated and applied to a historical dataset of tsunami magnitude scales. 
16:2017:10  Asymptotics for EBLUPs: Nested Error Regression Models Ziyang Lyu (Australian National University, Australia) and Alan Welsh (Australian National University, Australia) We derive the asymptotic distribution of estimated best linear unbiased predictors (EBLUPs) of the random effects in a nested error regression model. Under very mild conditions which do not require the assumption of normality, we show that asymptotically the distribution of the EBLUPs as both the number of clusters and the cluster sizes diverge to infinity is the convolution of the true distribution of the random effects and a normal distribution. This result yields very simple asymptotic approximations to and estimators of the prediction mean squared error of EBLUPs, and then asymptotic prediction intervals for the unobserved random effects. We provide a detailed theoretical and empirical comparison with the wellknown analytical prediction mean squared error approximations and estimators proposed by Kackar and Harville (1984) and Prasad and Rao (1990). We show that our simple estimator of the predictor mean squared errors of EBLUPs works very well in practice when both the number of clusters and the cluster sizes are sufficiently large. 
17:1017:50  Fast Spatial Additive Mixed Modeling for Large Samples Daisuke Murakami (The Institute of Statistical Mathematics, Japan) This study develops a spatial additive modeling (AMM) approach estimating spatial and nonspatial effects from large samples, such as millions of observations. The Morancoefficientbased approach is applied to model spatial effects flexibly. The proposed approach preconditions large matrices whose size grows with respect to the sample size N before the model estimation; thus, the computational complexity for the estimation is independent of the sample size. Furthermore, the preconditioning is done through a blockwise procedure that makes the memory consumption independent of N. Eventually, the spatial AMM is memoryfree and fast even for millions of observations. The developed approach is compared to alternatives through Monte Carlo simulation experiments. The result confirms the accuracy and computational efficiency of the developed approach. The developed approaches are implemented in an R package spmoran. 
18:30  Banquet 
27 March
Session 4 (10:3012:00)  Chairperson: Shogo Kato (The Institute of Statistical Mathematics) 

10:3011:10  A BiasReduced GARCHEVT Approach for Extreme Risk Estimations Hibiki Rhett Kaibuchi (SOKENDAI, Graduate University for Advanced Studies, Japan) Gilles Stupfler (École nationale de la statistique et de l’analyse de l’information, France), and Yoshinori Kawasaki (The Institute of Statistical Mathematics, Japan) Managing extreme event risk in finance and insurance is vital in our modern society. It is known that the statistically justifiable modeling and prediction of rare events are challenging because the historical data on extreme events are inherently scarce. In order to prevent or prepare for unfavorable scenarios, the approaches based on Extreme Value Theory (EVT) have been devised. In the literature on financial risk management, McNeil and Frey (2000, J. Empir. Finance) introduced the GARCHEVT approach. It is a twostep procedure consisting of GARCH filtering and PeaksOverThresholds method. It should be noted that one drawback of this methodology is that the correction of bias is not thoroughly considered. In this research, we propose a new way, as far as we aware, to estimate conditional ValueatRisk considering both bias correction and volatility background based on standard GARCHEVT approach. We illustrate the use of our approach in NASDAQ Index data. 
11:1012:00  What are the Structural Similarities and Differences between Tree Crowns and Biochar Pore Networks? A Twofold Answer from Fractal Analysis Pierre Dutilleul (McGill University, Canada) A priori, tree crowns and wood pellet biochar do not have much in common, except that the branching patterns of the former and the networks made of pores in the latter have a structural complexity that can be explored by fractal analysis. In this talk, I will explain how macro and microCT scanning technology (CT: computed tomography) can respectively be used to collect images and data to study these two types of structures. Statistical analysis is based on fractal geometry concepts and multifractal tools called “singularity spectrum” and “Rényi entropy spectrum”. The relevant statistical procedures and some newly developed software will be presented, and their application allows researchers to identify which structure, from tree crowns or biochar pore networks, is monofractal and which one is multifractal. This is joint work with Liwen Han (McGill University, Canada), Franziska Srocke (McGill University, Canada; University of Edinburgh, UK,), Ondrej Masek (University of Edinburgh, UK), and Donald Smith (McGill University, Canada). 
12:0013:30  Lunch Break 
13:3014:50  Facility tour in the ISM 
14:5015:00  Closing Address Alan Welsh (Australian National University) 
 Past Symposium

 2019/03/25 ISM Symposium on Environmental Statistics 2019
 2018/03/22 ISM Symposium on Environmental Statistics 2018
 2016/12/03 ANUUCISM Joint Symposium on Environmental Statistics
 2016/01/15 ISM Symposium on Environmental Statistics 2016
 2015/02/24 ISM Symposium on Environmental Statistics 2015
 2014/02/05 ISM Symposium on Environmental Statistics 2014
 2013/01/25 ISM Symposium on Environmental Statistics 2013