1.Flexible Modeling of Variable Asymmetries in Cross-Covariance Functions for Multivariate Random Fields. Ghulam A. QADIR, Carolina EUAN, and Ying SUN The geostatistical analysis of multivariate spatial data for inference as well as joint predictions (co-kriging) ordinarily relies on modeling of the marginal and cross-covariance functions. While the former quantifies the spatial dependence within variables, the latter quantifies the spatial dependence across distinct variables. The marginal covariance functions are always symmetric; however, the cross-covariance functions often exhibit asymmetries in the real data. Asymmetric cross-covariance implies change in the value of cross-covariance for interchanged locations on fixed order of variables. Such change of cross-covariance values is often caused due to the spatial delay in effect of the response of one variable on another variable. These spatial delays are common in environmental processes, especially when dynamic phenomena such as prevailing wind and ocean currents are involved. Here, we propose a novel approach to introduce flexible asymmetries in the cross-covariances of stationary multivariate covariance functions. The proposed approach involves modeling the phase component of the constrained cross-spectral features to allow for asymmetric cross-covariances. We show the capability of our proposed model to recover the cross-dependence structure and improve spatial predictions against traditionally used models through multiple simulation studies. Additionally, we illustrate our approach on a real trivariate dataset of particulate matter concentration (PM2,5), wind speed and relative humidity. The real data example shows that our approach outperforms the traditionally used models, in terms of model fit and spatial predictions. Supplementary materials accompanying this paper appear on-line. Key Words: Asymmetry; Coherence; Co-kriging; Matém covariance; Phase spectrum.--2. Statistical Downscaling with SpatialMisalignment: Application to Wildland Fire PM2.5 Concentration Forecasting Suman MAJUMDER, Yawen GUAN, Brian J. REICH, Susan O' NEILL, and Ana G. RAPPOLD Fine particulate matter, PM2_5, has been documented to have adverse health effects, and wildland fires are a major contributor to PM2 5 air pollution in the USA. Forecasters use numerical models to predict PM2 5 concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce bias and quantify uncertainty. Typical model calibration techniques do not allow for errors due to misalignment of geographic locations. We propose a spatiotemporal downscaling methodology that uses image registration techniques to identify the spatial misalignment and accounts for and corrects the bias produced by such warping. Our model is fitted in a Bayesian framework to provide uncertainty quantification of the misalignment and other sources of error. We apply this method to different simulated data sets and show enhanced performance of the method in presence of spatial misalignment. Finally, we apply the method to a large fire in Washington state and show that the proposed method provides more realistic uncertainty quantification than standard methods. Key Words: Image registration; Public health; Smoothing; Warping.--3. Nonparametric Bayesian Functional Meta-Regression: Applications in Environmental Epidemiology. Jaceun YU, Jinsu PARK, Taeryon CHOI, Masahiro HASHIZUME, Yoonhee Kio, Yasushi HONDA, and Yeonseung CHUNG Two-stage meta-analysis has been popularly used in epidemiological studies to investigate an association between environmental exposure and health response by analyzing time-series data collected from multiple locations. The first stage estimates the locationspecific association, while the second stage pools the associations across locations. The second stage often incorporates location-specific predictors (j.e., meta-predictors) to explain the between-location heterogeneity and is called meta-regression. The existing second-stage meta-regression relies on parametric assumptions and does not accommodate functional meta-predictors and spatial dependency. Motivated by these limitations, our research proposes a nonparametric Bayesian meta-regression which relaxes parametric assumptions and incorporates functional meta-predictors and spatial dependency. The proposed meta-regression is formulated by jointly modeling the association parameters and the functional meta-predictors using Dirichlet process (DP) or local DP mixtures. In doing so, the functional meta-predictors are represented parsimoniously by the coefficients of the orthonormal basis. The proposed models were applied to (1) a temperature-mortality association study and (2) suicide seasonality study, and validated through a simulation study. Supplementary materials accompanying this paper appear online. Key Words: Dirichlet process mixture; Functional predictor; Local Dirichlet process; Meta-regression; Spatial dependency.--4. A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data. Stephen N. FREEMAN, Nicholas J. B. ISAAC@, Panagiotis BESBEAS, Emily B. DENNIS, and Byron J. T. MORGAN Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species" indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data-only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided, Supplementary materials accompanying this paper appear online. Key Words: Bats; Butterflies; Dragonflies; Hidden Markov models; Hierarchical models; State-space models.--5. Bias Correction in Estimating Proportions Imperfect Pooled Testing. Graham HEPWORTH and Brad J. BIGGERSTAFF. In the estimation of proportions by pooled testing, the MLE is biased. Hepworth and Biggerstaff (JABES, 22:602-614, 2017) proposed an estimator based on the bias correction method of Firth (Biometrika 80:27-38, 1993) and showed that it is almost unbiased across a range of pooled testing problems involving no misclassification. We vow extend their work to allow for imperfect testing. We derive the estimator, provide a Newton-Raphson iterative formula for its computation and test it in situations involving equal or unequal pool sizes, drawing on problems encountered in plant disease assessment and prevalence estimation of mosquito-borne viruses. Our estimator is highly effective at reducing the bias for prevalences consistent with the pooled testing procedure employed. Key Words: Diagnostic testing; Firth's correction; Group testing; Sensitivity; Specificity.--6. Spatial Sampling Design Using Generalized Neyman-Scott Process. Sze Him LEUNG, Ji Meng LOH, Chun Yip YAU, and Zhengyuan ZHU In this paper we introduce a new procedure for spatial sampling design. It is found in previous studies (Zhu and Stein in J Agric Biol Environ Stat 11:24-44, 2006) that the optimal sampling design for spatial prediction with estimated parameters is nearly regular with a few clustered points.

The pattern is similar to a generalization of the Neyman-Scott (GNS) process (Yau and Loh in Statistica Sinica 22:1717-1736, 2012) which allows for regularity in the parent process. This motivates the use of a realization of the GNS process as sampling design points. This method translates the high-dimensional Optimization problem of selecting sampling sites into a low-dimensional optimization problem of searching for the optimal parameter sets in the GNS process. Simulation studies indicate that the proposed sampling design algorithm is more computationally efficient than traditional methods while achieving similar minimization of the criterion functions. While the traditional methods become computationally infeasible for sample size larger than a hundred, the proposed algorithm is applicable to a size as large as nm = 1024. A real data example of finding the optimal spatial design for predicting sea surface temperature in the Pacific Ocean is also considered. Key Words: Cross-entropy method; Geostatistics; Kriging; Neyman-Scott process; Matérn covariance function.