Open Access. Powered by Scholars. Published by Universities.®
- Discipline
Articles 1 - 27 of 27
Full-Text Articles in Entire DC Network
Letter To The Editor, Noel A. Cressie
Spatial Statistics In The Presence Of Location Error With An Application To Remote Sensing Of The Environment, Noel A. Cressie, John Kornak
Spatial Statistics In The Presence Of Location Error With An Application To Remote Sensing Of The Environment, Noel A. Cressie, John Kornak
Professor Noel Cressie
Techniques for the analysis of spatial data have, to date, tended to ignore any effect caused by error in specifying the spatial locations at which measurements are recorded. This paper reviews the methods for adjusting spatial inference in the presence of data-location error, particularly for data that. have a continuous spatial index (geostatistical data). New kriging equations are developed and evaluated based on a simulation experiment. They are also applied to remote-sensing data from the Total Ozone Mapping Spectrometer instrument on the Nimbus-7 satellite, where the location error is caused by assignment of the data to their nearest grid-cell centers. …
Small-Area Estimation: An Appraisal - Comment, Noel A. Cressie, Mark S. Kaiser
Small-Area Estimation: An Appraisal - Comment, Noel A. Cressie, Mark S. Kaiser
Professor Noel Cressie
Malay Ghosh and Jon Rao have presented us with a well written exposition of the topic of small area estimation. The past literature has been de-cidedly influenced by linear modeling, and we see that clearly in their paper. There has also been a tendency to judge the performance of the estimation methods by concentrating on a single, arbitrary small area. In our comment, we shall discuss what opportunities there might be to expand the class of statistical models for small area data and to consider multivariate aspects of small area estimation.
The Vprt - A Sequential Testing Procedure Dominating The Sprt, Noel A. Cressie, Peter Morgan
The Vprt - A Sequential Testing Procedure Dominating The Sprt, Noel A. Cressie, Peter Morgan
Professor Noel Cressie
Under more general assumptions than those usually made in the sequential analysis literature, a variable-sample-size-sequential probability ratio test (VPRT) of two simple hypotheses is found that maximizes the expected net gain over all sequential decision procedures. In contrast, Wald and Wolfowitz [25] developed the sequential probability ratio test (SPRT) to minimize expected sample size, but their assumptions on the parameters of the decision problem were restrictive. In this article we show that the expected net-gain-maximizing VPRT also minimizes the expected (with respect to both data and prior) total sampling cost and that, under slightly more general conditions than those imposed …
Spatial Mixture Models Based On Exponential Family Conditional Distributions, M Kaiser, Noel A. Cressie, J Lee
Spatial Mixture Models Based On Exponential Family Conditional Distributions, M Kaiser, Noel A. Cressie, J Lee
Professor Noel Cressie
Spatial statistical models are applied in many problems for which dependence in observed random variables is not easily explained by a direct scientific mechanism. In such situations there may be a latent spatial process that acts to produce the observed spatial pattern. Scientific interest often centers on the latent process and the degree of spatial dependence that characterizes it. Such latent processes may be thought of as spatial mixing distributions. We present methods for the specification of flexible joint distributions to model spatial processes through multi-parameter exponential family conditional distributions. One approach to the analysis of these models is Monte …
A Spatial Analysis Of Multivariate Output From Regional Climate Models, Stephan Sain, Reinhard Furrer, Noel A. Cressie
A Spatial Analysis Of Multivariate Output From Regional Climate Models, Stephan Sain, Reinhard Furrer, Noel A. Cressie
Professor Noel Cressie
Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output. However, there are often only a limited number of model runs available for a particular experiment, and one of the statistical challenges is to characterize the distribution of the model output. To that end, we have developed a multivariate hierarchical approach, at the heart of which is a new representation of a multivariate Markov random field. This approach allows for flexible modeling of the multivariate spatial …
A Spatial Analysis Of Variance Applied To Soil-Water Infiltration, C Gotway, Noel A. Cressie
A Spatial Analysis Of Variance Applied To Soil-Water Infiltration, C Gotway, Noel A. Cressie
Professor Noel Cressie
A spatial analysis of variance uses the spatial dependence among the observations to modify the usual interference procedures associated with a statistical linear model. When spatial correlation is present, the usual tests for presence of treatment effects may no longer be valid, and erroneous conclusions may result from assuming that the usual F ratios are F distributed. This is demonstrated using a spatial analysis of soil-water infiltration data. Emphasis is placed on modeling the spatial dependence structure with geostatistical techniques, and this spatial dependence structure is then used to test hypotheses about fixed effects using a nested linear model. -Authors
Size And Power Considerations For Testing Loglinear Models Using Divergence Test Statistics, Noel A. Cressie, L Pardo, M Del Carmen Pardo
Size And Power Considerations For Testing Loglinear Models Using Divergence Test Statistics, Noel A. Cressie, L Pardo, M Del Carmen Pardo
Professor Noel Cressie
In this article, we assume that categorical data are distributed according to a multinomial distribution whose probabilities follow a loglinear model. The inference problem we consider is that of hypothesis testing in a loglinear-model setting. The null hypothesis is a composite hypothesis nested within the alternative. Test statistics are chosen from the general class of divergence statistics. This article collects together the operating characteristics of the hypothesis test based on both asymptotic (using large-sample theory) and finite-sample (using a designed simulation study) results. Members of the class of power divergence statistics are compared, and it is found that the Cressie-Read …
Fast, Resolution-Consistent Spatial Prediction Of Global Processes From Satellite Data, Hsin-Cheng Huang, Noel A. Cressie, John Gabrosek
Fast, Resolution-Consistent Spatial Prediction Of Global Processes From Satellite Data, Hsin-Cheng Huang, Noel A. Cressie, John Gabrosek
Professor Noel Cressie
Polar orbiting satellites remotely sense the earth and its atmosphere, producing datasets that give daily global coverage. For any given day, the data are many and measured at spatially irregular locations. Our goal in this article is to predict values that are spatially regular at different resolutions; such values are often used as input to general circulation models (GCMs) and the like. Not only do we wish to predict optimally, but because data acquisition is relentless, our algorithm must also process the data very rapidly. This article applies a multiresolution autoregressive tree-structured model, and presents a new statistical prediction methodology …
Random Set Theory And Problems Of Modeling, Noel A. Cressie, G M. Laslett
Random Set Theory And Problems Of Modeling, Noel A. Cressie, G M. Laslett
Professor Noel Cressie
The three- or four-dimensional world in which we live is full of objects to be measured and summarized. Very often a parsimonious finite collection of measurements is enough for scientific investigation into an object’s genesis and evolution. There is a growing need, however, to describe and model objects through their form as well as their size. The purpose of this article is to show the potentials and limitations of a probabilistic and statistical approach. Collections of objects (the data) are assimilated to a random set (the model), whose parameters provide description and/or explanation.
Some Diagnostics For Markov Random Fields, Noel A. Cressie, Prasenjit Kapat
Some Diagnostics For Markov Random Fields, Noel A. Cressie, Prasenjit Kapat
Professor Noel Cressie
The development of diagnostics to check the fit of a proposed Markov random field (MRP) to data is a very important problem in spatial statistics. In this article, the consequences of fitting a given MRF to spatial data are visualized using diagnostic plots. The Gaussian MRF known as the conditional autoregressive model is featured. Various types of departures of the data from the fitted MRF model are calculated, allowing locally influential observations to be highlighted using the MRF-Neighborhoods plot. Through a global summary statistic and the Model-Comparison plot, we compare MRF models that differ both in terms of the neighborhood …
Long-Lead Prediction Of Pacific Ssts Via Bayesian Dynamic Modeling, L M. Berliner, Christopher K. Wikle, Noel A. Cressie
Long-Lead Prediction Of Pacific Ssts Via Bayesian Dynamic Modeling, L M. Berliner, Christopher K. Wikle, Noel A. Cressie
Professor Noel Cressie
Tropical Pacific sea surface temperatures (SSTs) and the accompanying El Nino-Southern Oscillation phenomenon are recognized as significant components of climate behavior. The atmospheric and oceanic processes involved display highly complicated variability over both space and time. Researchers have applied both physically derived modeling and statistical approaches to develop long-lead predictions of tropical Pacific SSTs. The comparative successes of these two approaches are a subject of substantial inquiry and some controversy. Presented in this article is a new procedure for long-lead forecasting of tropical Pacific SST fields that expresses qualitative aspects of scientific paradigms for SST dynamics in a statistical manner. …
Asymptotic Inference For Spatial Cdfs Over Time, Jun Zhu, S N. Lahiri, Noel A. Cressie
Asymptotic Inference For Spatial Cdfs Over Time, Jun Zhu, S N. Lahiri, Noel A. Cressie
Professor Noel Cressie
A spatial cumulative distribution function (SCDF) is a random function that provides a statistical summary of a random process over a spatial domain of interest. In this paper, we consider a spatio-temporal process and establish statistical methodology to analyze changes in the SCDF over time. We develop hypothesis testing to detect a difference in the spatial random processes at two time points, and we construct a prediction interval to quantify such discrepancy in the corresponding SCDFs. Using a spatial subsampling method, we show that our inferences are valid asymptotically. As an illustration, we apply these inference procedures to test and …
Minimum Phi Divergence Estimator And Hierarchical Testing In Loglinear Models, Noel A. Cressie, Leandro Pardo
Minimum Phi Divergence Estimator And Hierarchical Testing In Loglinear Models, Noel A. Cressie, Leandro Pardo
Professor Noel Cressie
In this paper we consider inference based on very general divergence measures, under assumptions of multinomial sampling and loglinear models. We define the minimum phi divergence estimator, which is seen to be a generalization of the maximum likelihood estimator. This estimator is then used in a phi divergence goodness-of-fit statistic, which is the basis of two new statistics for solving the problem of testing a nested sequence of loglinear models.
A Robust-Resistant Spatial Analysis Of Soil Water Infiltration., Noel A. Cressie, R Horton
A Robust-Resistant Spatial Analysis Of Soil Water Infiltration., Noel A. Cressie, R Horton
Professor Noel Cressie
Concentrates on estimating the spatial correlations between soil water infiltration observations, with special emphasis on resistant methods to remove nonstationarity. After this removal, robust semivariogram estimators are used to examine the spatial dependencies for various tillage treatments. There is some indication that infiltration characteristics inherit different types of spatial dependency, depending on the tillage treatment applied.-from Authors
Testing For Activation In Data From Fmri Experiments, Martina Pavlicova, Noel A. Cressie, Thomas J. Santner
Testing For Activation In Data From Fmri Experiments, Martina Pavlicova, Noel A. Cressie, Thomas J. Santner
Professor Noel Cressie
The traditional method for processing functional magnetic resonance imaging (FMRI) data is based on a voxel-wise, general linear model. For experiments conducted using a block design, where periods of activation are interspersed with periods of rest, a haemodynamic response function (HRF) is convolved with the design function and, for each voxel, the convolution is regressed on prewhitened data. An initial analysis of the data often involves computing voxel-wise two-sample t-tests, which avoids a direct specification of the HRF. Assuming only the length of the haemodynamic delay is known, scans acquired in transition periods between activation and rest are omitted, and …
Asymptotic Properties Of Maximum (Composite) Likelihood Estimators For Partially Ordered Markov Models, Hsin-Cheng Huang, Noel A. Cressie
Asymptotic Properties Of Maximum (Composite) Likelihood Estimators For Partially Ordered Markov Models, Hsin-Cheng Huang, Noel A. Cressie
Professor Noel Cressie
Partially ordered Markov models (POMMs) are Markov random fields (MRFs) with neighborhood structures derivable from an associated partially ordered set. The most attractive feature of POMMs is that their joint distributions can be written in closed and product form. Therefore, simulation and maximum likelihood estimation for the models is quite straightforward, which is not the case in general for MRF models. In practice, one often has to modify the likelihood to account for edge components; the resulting composite likelihood for POMMs is similarly straightforward to maximize. In this article, we use a martingale approach to derive the asymptotic properties of …
Flexible Spatial Models For Kriging And Cokriging Using Moving Averages And The Fast Fourier Transform (Fft), Jay M. Ver Hoef, Noel A. Cressie, Ronald P. Barry
Flexible Spatial Models For Kriging And Cokriging Using Moving Averages And The Fast Fourier Transform (Fft), Jay M. Ver Hoef, Noel A. Cressie, Ronald P. Barry
Professor Noel Cressie
Models for spatial autocorrelation and cross-correlation depend on the distance and direction separating two locations, and are constrained so that for all possible sets of locations, the covariance matrices implied from the models remain nonnegative-definite. Based on spatial correlation, optimal linear predictors can be constructed that yield complete maps of spatial fields from incomplete and noisy spatial data. This methodology is called kriging if the data are of only one variable type, and it is called cokriging if it is of two or more variable types. Historically, to satisfy the nonnegative-definite condition, cokriging has used coregionalization models for cross-variograms, even …
Fixed Rank Filtering For Spatio-Temporal Data, Noel Cressie, Tao Shi, Emily L. Kang
Fixed Rank Filtering For Spatio-Temporal Data, Noel Cressie, Tao Shi, Emily L. Kang
Professor Noel Cressie
Datasets from remote-sensing platforms and sensor networks are often spatial, temporal, and very large. Processing massive amounts of data to provide current estimates of the (hidden) state from current and past data is challenging, even for the Kalman filter. A large number of spatial locations observed through time can quickly lead to an overwhelmingly high-dimensional statistical model. Dimension reduction without sacrificing complexity is our goal in this article. We demonstrate how a Spatio-Temporal Random Effects (STRE) component of a statistical model reduces the problem to one of fixed dimension with a very fast statistical solution, a methodology we call Fixed …
Combining Outputs From The North American Regional Climate Change Assessment Program By Using A Bayesian Hierarchical Model, Emily L. Kang, Noel Cressie, Stephan R. Sain
Combining Outputs From The North American Regional Climate Change Assessment Program By Using A Bayesian Hierarchical Model, Emily L. Kang, Noel Cressie, Stephan R. Sain
Professor Noel Cressie
We investigate the 20-year-average boreal winter temperatures generated by an ensemble of six regional climate models (RCMs) in phase I of the North American Regional Climate Change Assessment Program. We use the long-run average (20-year integration) to smooth out variability and to capture the climate properties from the RCM outputs. We find that, although the RCMs capture the large-scale climate variation from coast to coast and from south to north similarly, their outputs can differ substantially in some regions. We propose a Bayesian hierarchical model to synthesize information from the ensemble of RCMs, and we construct a consensus climate signal …
Modeling Dynamic Controls On Ice Streams: A Bayesian Statistical Approach, L Mark Berliner, Kenneth Jezek, Noel Cressie, Yongku Kim, Calvin Lam, Cornelis Van Der Veen
Modeling Dynamic Controls On Ice Streams: A Bayesian Statistical Approach, L Mark Berliner, Kenneth Jezek, Noel Cressie, Yongku Kim, Calvin Lam, Cornelis Van Der Veen
Professor Noel Cressie
Our main goal is to exemplify the study of ice-stream dynamics via Bayesian statistical analysis incorporating physical, though imperfectly known, models using data that are both incomplete and noisy. The physical-statistical models we propose account for these uncertainties in a coherent, hierarchical manner. The initial modeling assumption estimates basal shear stress as equal to driving stress, but subsequently includes a random corrector process to account for model error. The resulting stochastic equation is incorporated into a simple model for surface velocities. Use of Bayes' theorem allows us to make inferences on all unknowns given basal elevation, surface elevation and surface …
A Method For Evaluating Bias In Global Measurements Of Co2 Total Columns From Space, D Wunch, P O. Wennberg, G C. Toon, B J. Connor, B Fisher, G B. Osterman, C Frankenberg, L Mandrake, C O'Dell, P Ahonen, S C. Biraud, R Castano, Noel Cressie, D Crisp, N M. Deutscher, A Eldering, M L. Fisher, David W. Griffith, M Gunson, P Heikkinen, G Keppel-Aleks, E Kyro, R Lindemaier, Ronald Macatangay, J Mendonca, J Messerschmidt, C E. Miller, I Morino, J Notholt, F A. Oyafuso, M Rettinger, J Robinson, C M. Roehl, R J. Salawitch, V Sherlock, K Strong, R Sussmann, T Tanaka, D R. Thompson, O Uchino, Thorsten Warneke, Steven C. Wofsy
A Method For Evaluating Bias In Global Measurements Of Co2 Total Columns From Space, D Wunch, P O. Wennberg, G C. Toon, B J. Connor, B Fisher, G B. Osterman, C Frankenberg, L Mandrake, C O'Dell, P Ahonen, S C. Biraud, R Castano, Noel Cressie, D Crisp, N M. Deutscher, A Eldering, M L. Fisher, David W. Griffith, M Gunson, P Heikkinen, G Keppel-Aleks, E Kyro, R Lindemaier, Ronald Macatangay, J Mendonca, J Messerschmidt, C E. Miller, I Morino, J Notholt, F A. Oyafuso, M Rettinger, J Robinson, C M. Roehl, R J. Salawitch, V Sherlock, K Strong, R Sussmann, T Tanaka, D R. Thompson, O Uchino, Thorsten Warneke, Steven C. Wofsy
Professor Noel Cressie
We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO2 (XCO2) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in XCO2 south of 25S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth …
Dynamical Random-Set Modeling Of Concentrated Precipitation In North America, Noel Cressie, Renato Assuncao, Scott H. Holan, Michael Levine, Orietta Nicolis, Jun Zhang, Jian Zou
Dynamical Random-Set Modeling Of Concentrated Precipitation In North America, Noel Cressie, Renato Assuncao, Scott H. Holan, Michael Levine, Orietta Nicolis, Jun Zhang, Jian Zou
Professor Noel Cressie
In order to study climate at scales where policy decisions can be made, regional climate models (RCMs) have been developed with much finer resolution (~50 km) than the ~500 km resolution of atmosphere-ocean general circulation models (AOGCMs). The North American Regional Climate Change Assessment Program (NARCCAP) is an international program that provides 50-km resolution climate output for the United States, Canada, and northern Mexico. In Phase I, there are six RCMs, from which we choose one to illustrate our methodology. The RCMs are updated every 3 hours and contain a number of variables, including temperature, precipitation, wind speed, wind direction, …
Rejoinder, Peter Craigmile, Catherine Caldert, Hongfei Li, Rajib Paul, Noel Cressie
Rejoinder, Peter Craigmile, Catherine Caldert, Hongfei Li, Rajib Paul, Noel Cressie
Professor Noel Cressie
We agree with Schmidt that it is essential that researchers from many diverse areas have access to affordable, but still trustworthy, software. In this research project, substantial effort went into preparing datasets. Much of the data came from different government agencies, with databases arranged in multiple formats, often including variables that were not immediately relevant to our scientific pursuits. In our work on this project, the use of SAS was essential to producing clean datasets.
Hierarchical Model Building, Fitting, And Checking: A Behind-The-Scenes Look At A Bayesian Analysis Of Arsenic Exposure Pathways, Peter F. Craigmile, Catherine A. Calder, Hongfei Li, Rajib Paul, Noel Cressie
Hierarchical Model Building, Fitting, And Checking: A Behind-The-Scenes Look At A Bayesian Analysis Of Arsenic Exposure Pathways, Peter F. Craigmile, Catherine A. Calder, Hongfei Li, Rajib Paul, Noel Cressie
Professor Noel Cressie
In this article, we present a behind-the-scenes look at a Bayesian hierarchical analysis of pathways of exposure to arsenic (a toxic heavy metal) using the Phase I National Human Exposure Assessment Survey carried out in Arizona. Our analysis combines individual-level personal exposure measurements (biomarker and environmental media) with water, soil, and air observations from the ambient environment. We include details of our model-building exercise that involved a combination of exploratory data analysis and substantive knowledge in exposure science. Then we present our strategies for model fitting, which involved piecing together components of the hierarchical model in a systematic fashion to …
Accounting For Uncertainty In Ecological Analysis: The Strengths And Limitations Of Hierarchical Statistical Modeling, Noel Cressie, Catherine Calder, James Clark, Jay Ver Hoef, Christopher Wikle
Accounting For Uncertainty In Ecological Analysis: The Strengths And Limitations Of Hierarchical Statistical Modeling, Noel Cressie, Catherine Calder, James Clark, Jay Ver Hoef, Christopher Wikle
Professor Noel Cressie
Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. Measurement and/or process errors are often the only sources of uncertainty modeled when addressing complex ecological problems, yet analyses should also account for uncertainty in sampling design, in model specification, in parameters governing the specified model, and in initial and boundary conditions. Only then can we be confident in the scientific inferences and forecasts made from an analysis. Probability and statistics provide a framework that accounts for multiple …
Spectral Density Estimation Through A Regularized Inverse Problem, Chunfeng Huang, Tailen Hsing, Noel Cressie
Spectral Density Estimation Through A Regularized Inverse Problem, Chunfeng Huang, Tailen Hsing, Noel Cressie
Professor Noel Cressie
In the study of stationary stochastic processes on the real line, the covariance function and the spectral density function are parameters of considerable interest. They are equivalent ways of expressing the temporal dependence in the process. In this article, we consider the spectral density function and propose a new estimator that is not based on the periodogram; the estimator is derived through a regularized inverse problem. A further feature of the estimator is that the data are not required to be observed on a grid. When the regularization condition is based on the function's first derivative, we give the estimator …