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Integrating hierarchical statistical models and Machine-Learning Algorithms for Ground-Truthing Drone Images of the Vegetation: Taxonomy, Abundance and Population Ecological models

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REMOTE SENSING 2021年第6期13卷 1161-1161页

作者： Damgaard, Christian Aarhus Univ Dept Biosci Vejlsovej 25 DK-8600 Silkeborg Denmark

In order to fit population ecological models, e.g., plant competition models, to new drone-aided image data, we need to develop statistical models that may take the new type of measurement uncertainty when applying machine-learning algorithms into account and quantify its importance for statistical inferences and ecological predictions. Here, it is proposed to quantify the uncertainty and bias of image predicted plant taxonomy and abundance in a hierarchical statistical model that is linked to ground-truth data obtained by the pin-point method. It is critical that the error rate in the species identification process is minimized when the image data are fitted to the population ecological models, and several avenues for reaching this objective are discussed. The outlined method to statistically model known sources of uncertainty when applying machine-learning algorithms may be relevant for other applied scientific disciplines.

关键词： measurement uncertainty machine-learning algorithms plant competition models hierarchical statistical model

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Emission factors determination of Euro III 1,200- to 1,400-cc petrol passenger cars with a PLS multivariate regression model

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ENVIRONMENTAL modelING & ASSESSMENT 2008年第3期13卷 383-392页

作者： Rapone, M. Prati, M. V. Costagliola, M. A. Della Ragione, L. Meccariello, G. CNR Ist Motori I-80125 Naples Italy

This paper presents emission factors of a class of passenger cars obtained by applying a statistical model developed to evaluate average emission factors based on driving cycle emission measurements. A multivariate regression method based on principal components, namely, the partial least squares (PLS) method, is applied to calculate the model. The method was applied to emission data from a sample of petrol Euro III 1,200- to 1,400-cc passenger cars taken from the ARTEMIS database. A vehicle effect analysis showed that vehicle effect is considerable, in some cases comparable to or greater than the driving cycle effect. Determination of emission factors is obviously affected by these aspects. Thus, the CO2 PLS model fit results are good, CO, HC and NOX more or less sufficient. PLS-predicted quantities were compared with corresponding quantities estimated by a multiple regression model (GLM) based on a quadratic polynomial equation of sub-cycle overall mean speed. GLM goodness of fit was poorer than PLS ones. A validation effort of models is in progress, which is considering the ARTEMIS database extended with tests performed within other national or international projects. In this way, an extended population of combinations of vehicles and driving cycles will provide a better calculation of models and emission factors.

关键词： emission factors ARTEMIS hierarchical statistical model partial least squares (PLS)

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Waypoint analysis for Command and Control

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NAVAL RESEARCH LOGISTICS 2004年第8期51卷 1045-1067页

作者： Wendt, DA Irwin, ME Cressie, N Ohio State Univ Dept Stat Columbus OH 43210 USA Battelle Mem Inst Columbus OH 43201 USA

Command and Control (C2) in a military setting can be epitomized in battles-of-old when commanders would seek high ground to gain superior spatial-temporal information;from this vantage point, decisions were made and relayed to units in the field. Although the fundamentals remain, technology has changed the practice of C2;for example, enemy units may be observed remotely, with instruments of varying positional accuracy. A basic problem in C2 is the ability to track an enemy object in the battlespace and to forecast its future position;the (extended) Kalman filter provides a straightforward solution. The problem changes fundamentally if one assumes that the moving object is headed for an (unknown) location, or waypoint. This article is concerned with the new problem of estimation of such a waypoint, for which we use Bayesian statistical prediction. The computational burden is greater than an ad hoc regression-based estimate, which we also develop, but the Bayesian approach has a big advantage in that it yields both a predictor and a measure of its variability. (C) 2004 Wiley Periodicals, Inc.

关键词： battlespace Bayesian analysis C2 spatio-temporal prediction hierarchical statistical model

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Two-scale spatial models for binary data

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statistical METHODS AND APPLICATIONS 2018年第1期27卷 1-24页

作者： Hardouin, Cecile Cressie, Noel Univ Paris Nanterre MODALX Paris France Univ Wollongong Sch Math & Appl Stat NIASRA Wollongong NSW Australia

A spatial lattice model for binary data is constructed from two spatial scales linked through conditional probabilities. A coarse grid of lattice locations is specified, and all remaining locations (which we call the background) capture fine-scale spatial dependence. Binary data on the coarse grid are modelled with an autologistic distribution, conditional on the binary process on the background. The background behaviour is captured through a hidden Gaussian process after a logit transformation on its Bernoulli success probabilities. The likelihood is then the product of the (conditional) autologistic probability distribution and the hidden Gaussian-Bernoulli process. The parameters of the new model come from both spatial scales. A series of simulations illustrates the spatial-dependence properties of the model and likelihood-based methods are used to estimate its parameters. Presence-absence data of corn borers in the roots of corn plants are used to illustrate how the model is fitted.

关键词： Auto-logistic model EM algorithm Gaussian process hierarchical statistical model Laplace approximation Spatial odds-ratio

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Bayesian Inference of Spatio-Temporal Changes of Arctic Sea Ice

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BAYESIAN ANALYSIS 2020年第2期15卷 605-631页

作者： Zhang, Bohai Cressie, Noel Nankai Univ KLMDASR Sch Stat & Data Sci Tianjin 300071 Peoples R China Nankai Univ LPMC Tianjin 300071 Peoples R China Univ Wollongong Natl Inst Appl Stat Res Australia Wollongong NSW 2522 Australia

Arctic sea ice extent has drawn increasing interest and alarm from geoscientists, owing to its rapid decline. In this article, we propose a Bayesian spatio-temporal hierarchical statistical model for binary Arctic sea ice data over two decades, where a latent dynamic spatio-temporal Gaussian process is used to model the data-dependence through a logit link function. Our ultimate goal is to perform inference on the dynamic spatial behavior of Arctic sea ice over a period of two decades. Physically motivated covariates are assessed using autologistic diagnostics. Our Bayesian spatio-temporal model shows how parameter uncertainty in such a complex hierarchical model can influence spatio-temporal prediction. The posterior distributions of new summary statistics are proposed to detect the changing patterns of Arctic sea ice over two decades since 1997.

关键词： binary data forecasting hierarchical statistical model latent Gaussian process MCMC

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Measuring, mapping, and uncertainty quantification in the space-time cube

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REVISTA MATEMATICA COMPLUTENSE 2020年第3期33卷 643-660页

作者： Cressie, Noel Wikle, Christopher K. Univ Wollongong Wollongong NSW Australia Univ Missouri Columbia MO USA

The space-time cube is not a cube of course, but the idea of one is useful. Its base is a spatial domain, D-t, and the "cube" is traced out by a process of spatial domains, {D-t : t >= 0}. Now fill the cube with a spatio-temporal stochastic process {Y-t (s) : s is an element of D-t, t >= 0}. Assume that {D-t} is fixed and known (but clearly it too could be stochastic). Slicing the cube laterally for a fixed t(0) generates a spatial stochastic process {Y-t0 (s) : s is an element of D-t0}. Slicing the cube longitudinally for a fixed s(0) generates a temporal process {Y-t (s(0)) : t >= 0} that, after dicing, yields a time series, {Y-0(s(0)), Y-1(s(0)),...}. These are the main highways that traverse the cube but other, less-traveled paths, can be taken. In this paper, we discuss spatio-temporal data and processes whose domain is the space-time cube, and we incorporate them into hierarchical statistical models for spatio-temporal data.

关键词： Change-of-support Uncertainty quantification Space-time interaction hierarchical statistical model Deep neural models Stochastic PDE

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Polynomial nonlinear spatio-temporal integro-difference equation models

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JOURNAL OF TIME SERIES ANALYSIS 2011年第4期32卷 339-350页

作者： Wikle, Christopher K. Holan, Scott H. Univ Missouri Dept Stat Columbia MO 65211 USA

Spatio-temporal statistical models are ubiquitous in the environmental sciences. Often the underlying spatio-temporal process can be written as a hierarchical state-space process that utilizes prior understanding of the physical process to formulate models for the state transition. In situations with complicated dynamics, such as wave propagation, or population growth and dispersal models, parameterizing the transition function associated with the high-dimensional state process may prove difficult or even impossible. One approach to overcoming this difficulty is through the use of polynomial stochastic integro-difference equation (IDE) models. Here, in the context of discrete time and continuous space, complicated dynamics can be accommodated through redistribution kernels that are allowed to vary with space. To facilitate computation and dimension reduction, we consider a basis-function-expansion representation of this model. Even in this framework there are too many parameters to estimate efficiently from a classical perspective. Therefore, we utilize the hierarchical Bayesian framework in order to implement a stochastic-search variable-selection algorithm that mitigates these estimation issues. Finally, the method is illustrated on the problem of long-lead prediction of equatorial sea surface temperature.

关键词： Bayesian hierarchical statistical model quadratic nonlinearity sea surface temperature spectral stochastic search variable selection

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EFFICIENT INFERENCE FOR SPATIAL AND SPATIO-TEMPORAL statistical modelS USING BASIS-FUNCTION AND DEEP-LEARNING METHODS

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BULLETIN OF THE AUSTRALIAN MATHEMATICAL SOCIETY 2024年第1期111卷 182-183页

作者： Sainsbury-Dale, Matthew Univ Wollongong Sch Math & Appl Stat Wollongong NSW 2522 Australia

Inference in spatial and spatio-temporal models can be challenging for a variety of reasons. For example, non-Gaussianity often leads to analytically intractable integrals;we may be in a 'big' data setting, whereby the number of observations renders traditional methods too computationally expensive;we may wish to make inferences over spatial supports that are different to those of our measurements;or, we may wish to use a statistical model whose likelihood function is either unavailable or computationally intractable. In this thesis, I develop several techniques that help to alleviate these challenges.

关键词： amortised inference Bayes estimator change-of-support extreme-value model hierarchical statistical model inverse problem likelihood-free inference non-Gaussian data

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Mission CO₂ntrol: A statistical Scientist's Role in Remote Sensing of Atmospheric Carbon Dioxide

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JOURNAL OF THE AMERICAN statistical ASSOCIATION 2018年第521期113卷 152-168页

作者： Cressie, Noel Univ Wollongong Sch Math & Appl Stat Natl Inst Appl Stat Res Australia Wollongong NSW 2522 Australia

Too much carbon dioxide (CO2) in the atmosphere is a threat to long-term sustainability of Earth's ecosystem. Atmospheric CO2 is a leading greenhouse gas that has increased to levels not seen since the middle Pliocene (approximately 3.6 million years ago). One of the US National Aeronautics Space Administration's (NASA) remote sensing missions is the Orbiting Carbon Observatory-2, whose principal science objective is to estimate the global geographic distribution of CO2 sources and sinks at Earth's surface, through time. This starts with raw radiances (Level 1), moves on to retrievals of the atmospheric state (Level 2), from which maps of gap-filled and de-noised geophysical variables and their uncertainties are made (Level 3). With the aid of a model of transport in the atmosphere, CO2 fluxes (Level 4) can be obtained from Level 2 data directly or possibly through Level 3. Decisions about how to mitigate or manage CO2 could be thought of as Level 5. hierarchical statistical modeling is used to qualify and quantify the uncertainties at each level. Supplementary materials for this article are available online.

关键词： Climate change CO2 flux inversion hierarchical statistical model Kriging Orbiting Carbon Observatory-2 Predictive distribution

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Efficient estimation of distributional properties of extreme seas from a hierarchical description applied to calculation of un-manning and other weather-related operational windows

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OCEAN ENGINEERING 2021年 238卷 109642-109642页

作者： Towe, Ross Zanini, Elena Randell, David Feld, Graham Jonathan, Philip Shell Res Ltd London SE1 7NA England Shell Global Solut Int BV NL-1031 HW Amsterdam Netherlands Shell UK Ltd Aberdeen AB12 3FY Scotland Univ Lancaster Dept Math & Stat Lancaster LA1 4YF England

Methods of computational statistics allow efficient estimation of extreme ocean environments, and facilitate optimal operational decision making. We describe estimation of extreme quantiles of total water level and related quantities from a non-stationa r y hierarchical model for ocean storms. The model incorporates a directional-seasonal extreme value model for occurrences of storm peak significant wave height, a conditional directional model for within-storm evolution of sea states relative to storm peak, a conditional model for the maximum crest within a sea state, and models for total water level. Importance sampling is used for efficient computation of marginal total water level characteristics. We use the model to estimate an optimal un-manning procedure for a notional North Sea offshore structu r e in severe conditions.

关键词： hierarchical statistical model Extreme Retur n value Covariate Importance sampling Weather window Un-manning

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