According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

Statistics for Spatial Data.

Structured additive regression models are perhaps the most commonly used class of models in statistical applications. It includes, among others, (generalized) linear models, (generalized) additive models, smoothing spline models, state space models, semiparametric regression, spatial and spatiotemporal models, log-Gaussian Cox processes and geostatistical and geoadditive models. We consider approximate Bayesian inference in a popular subset of structured additive regression models, latent Gaussian models, where the latent field is Gaussian, controlled by a few hyperparameters and with non-Gaussian response variables. The posterior marginals are not available in closed form owing to the non-Gaussian response variables. For such models, Markov chain Monte Carlo methods can be implemented, but they are not without problems, in terms of both convergence and computational time. In some practical applications, the extent of these problems is such that Markov chain Monte Carlo sampling is simply not an appropriate tool for routine analysis. We show that, by using an integrated nested Laplace approximation and its simplified version, we can directly compute very accurate approximations to the posterior marginals. The main benefit of these approximations is computational: where Markov chain Monte Carlo algorithms need hours or days to run, our approximations provide more precise estimates in seconds or minutes. Another advantage with our approach is its generality, which makes it possible to perform Bayesian analysis in an automatic, streamlined way, and to compute model comparison criteria and various predictive measures so that models can be compared and the model under study can be challenged.

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Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations

Air pollution and health.

The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.

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The Environmental Protection Agency

Introduction Historical Introduction, Peter J. Diggle Continuous Spatial Variation Continuous Parameter Stochastic Process Theory, Tilmann Gneiting and Peter Guttorp Classical Geostatistical Methods, Dale L. Zimmerman and Michael Stein Likelihood-Based Methods, Dale L. Zimmerman Spectral Domain, Montserrat Fuentes and Brian Reich Asymptotics for Spatial Processes, Michael Stein Hierarchical Modeling with Spatial Data, Christopher K. Wikle Low Rank Representations for Spatial Processes, Christopher K. Wikle Constructions for Nonstationary Spatial Processes, Paul D. Sampson Monitoring Network Design, James V. Zidek and Dale L. Zimmerman Non-Gaussian and Nonparametric Models for Continuous Spatial Data, Mark F.J. Steel and Montserrat Fuentes Discrete Spatial Variation Discrete Spatial Variation, Havard Rue and Leonard Held Conditional and Intrinsic Autoregressions, Leonhard Held and Havard Rue Disease Mapping, Lance Waller and Brad Carlin Spatial Econometrics, R. Kelley Pace and James LeSage Spatial Point Patterns Spatial Point Process Theory, Marie-Colette van Lieshout Spatial Point Process Models, Valerie Isham Nonparametric Methods, Peter J. Diggle Parametric Methods, Jesper Moller Modeling Strategies, Adrian Baddeley Multivariate and Marked Point Processes, Adrian Baddeley Point Process Models and Methods in Spatial Epidemiology, Lance Waller Spatio-Temporal Processes Continuous Parameter Spatio-Temporal Processes, Tilmann Gneiting and Peter Guttorp Dynamic Spatial Models Including Spatial Time Series, Dani Gamerman Spatio-Temporal Point Processes, Peter J. Diggle and Edith Gabriel Modeling Spatial Trajectories, David R. Brillinger Data Assimilation, Douglas W. Nychka and Jeffrey L. Anderson Additional Topics Multivariate Spatial Process Models, Alan E. Gelfand and Sudipto Banerjee Misaligned Spatial Data: The Change of Support Problem, Alan E. Gelfand Spatial Aggregation and the Ecological Fallacy, Jonathan Wakefield and Hilary Lyons Spatial Gradients and Wombling, Sudipto Banerjee Index

Handbook of spatial statistics

SUMMARY We propose a nonstationary periodogram and various parametric approaches for estimating the spectral density of a nonstationary spatial process. We also study the asymptotic properties of the proposed estimators via shrinking asymptotics, assuming the distance between neighbouring observations tends to zero as the size of the observation region grows without bound. With this type of asymptotic model we can uniquely determine the spectral density, avoiding the aliasing problem. We also present a new class of nonstationary processes, based on a convolution of local stationary processes. This model has the advantage that the model is simultaneously defined everywhere, unlike 'moving window' approaches, but it retains the attractive property that, locally in small regions, it behaves like a stationary spatial process. Applications include the spatial analysis and modelling of air pollution data provided by the US Environmental Protection Agency.

http://ces.stat.ucla.edu/page_attachments/0000/0042/fuents.pdf

Spectral methods for nonstationary spatial processes

SUMMARY. Constructing maps of dry deposition pollution levels is vital for air quality management, and presents statistical problems typical of many environmental and spatial applications. Ideally, such maps would be based on a dense network of monitoring stations, but this does not exist. Instead, there are two main sources of information for dry deposition levels in the United States: one is pollution measurements at a sparse set of about 50 monitoring stations called CASTNet, and the other is the output of the regional scale air quality models, called Models-3. A related problem is the evaluation of these numerical models for air quality applications, which is crucial for control strategy selection. We develop formal methods for combining sources of information with different spatial resolutions and for the evaluation of numerical models. We specify a simple model for both the Models-3 output and the CASTNet observations in terms of the unobserved ground truth, and we estimate the model in a Bayesian way. This provides improved spatial prediction via the posterior distribution of the ground truth, allows us to validate Models-3 via the posterior predictive distribution of the CASTNet observations, and enables us to remove the bias in the Models-3 output. We apply our methods to data on SO2 concentrations, and we obtain high-resolution SO2 distributions by combining observed data with model output. We also conclude that the numerical models perform worse in areas closer to power plants, where the SO2 values are overestimated by the models.

https://www.stat.washington.edu/raftery/Research/PDF/fuentes2005.pdf

Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models.

Likelihood approaches for large, irregularly spaced spatial datasets are often very difficult, if not infeasible, to implement due to computational limitations. Even when we can assume normality, exact calculations of the likelihood for a Gaussian spatial process observed at n locations requires O(n3) operations. We present a version of Whittle's approximation to the Gaussian log-likelihood for spatial regular lattices with missing values and for irregularly spaced datasets. This method requires O(nlog2n) operations and does not involve calculating determinants. We present simulations and theoretical results to show the benefits and the performance of the spatial likelihood approximation method presented here for spatial irregularly spaced datasets and lattices with missing values. We apply these methods to estimate the spatial structure of sea surface temperatures using satellite data with missing values.

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Approximate likelihood for large irregularly spaced spatial data.

Tropospheric ozone is one of the six criteria pollutants regulated by the United States Environmental Protection Agency under the Clean Air Act and has been linked with several adverse health effects, including mortality. Due to the strong dependence on weather conditions, ozone may be sensitive to climate change and there is great interest in studying the potential effect of climate change on ozone, and how this change may affect public health. In this paper we develop a Bayesian spatial model to predict ozone under different meteorological conditions, and use this model to study spatial and temporal trends and to forecast ozone concentrations under different climate scenarios. We develop a spatial quantile regression model that does not assume normality and allows the covariates to affect the entire conditional distribution, rather than just the mean. The conditional distribution is allowed to vary from site-to-site and is smoothed with a spatial prior. For extremely large datasets our model is computat...

/pdf/bayesian-spatial-quantile-regression-8s7vdrbjca.pdf

Montserrat Fuentes

Papers

Handbook of spatial statistics

Spectral methods for nonstationary spatial processes

Model evaluation and spatial interpolation by Bayesian combination of observations with outputs from numerical models.

Approximate likelihood for large irregularly spaced spatial data.

Bayesian Spatial Quantile Regression