A comparison of Bayesian models for daily ozone concentration levels

Sahu, Sujit K.; Bakar, K. Shuvo

doi:10.1016/j.stamet.2011.04.009

Cited by 28 publications

(31 citation statements)

References 13 publications

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“…We use a real life data set, previously analysed by Sahu and Bakar (2012a), on daily maximum 8-hour average ground level ozone concentration for the months of July and August in 2006, observed at 28 monitoring sites in the state of New York. We consider three important covariates: maximum temperature (cMAXTEMP in degree Celsius), wind speed (WDSP in nautical miles) and percentage average relative humidity (RH) for building a spatio-temporal model for ozone concentration.…”

Section: A Practical Examplementioning

confidence: 99%

“…The linear (covariate) part of the model is specified by the formula argument that automatically includes the intercept. Secondly, the square-root transformation is used, on the fly, to stabilize the variance (Sahu et al 2007;Sahu and Bakar 2012a). The initial values and the values of the hyper-parameters for the prior distributions are assumed by default.…”

Section: A Practical Examplementioning

confidence: 99%

“…An explicit auto-regressive term for the underlying true spatio-temporal process is assumed in a hierarchical set-up that includes the overall nugget effect. This model is included in the spTimer package because of its superior performance in modeling air-pollution data, see e.g., Cameletti, Ignaccolo, and Bande (2009) and Sahu and Bakar (2012a). This auto-regressive (AR) model is modified, as the third and final implemented model, to include the recently developed Gaussian predictive process approximation technique for handling large spatial and spatio-temporal data following Banerjee, Gelfand, Finley, and Sang (2008), Finley, Sang, Banerjee, and Gelfand (2009) and Sahu and Bakar (2012b).…”

Section: Introductionmentioning

confidence: 99%

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spTimer: Spatio-Temporal Bayesian Modeling UsingR

Bakar¹,

Sahu²

2015

J. Stat. Soft.

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Hierarchical Bayesian modeling of large point-referenced space-time data is increasingly becoming feasible in many environmental applications due to the recent advances in both statistical methodology and computation power. Implementation of these methods using the Markov chain Monte Carlo (MCMC) computational techniques, however, requires development of problem-specific and user-written computer code, possibly in a low-level language. This programming requirement is hindering the widespread use of the Bayesian model-based methods among practitioners and, hence there is an urgent need to develop high-level software that can analyze large data sets rich in both space and time.This paper develops the package spTimer for hierarchical Bayesian modeling of stylized environmental space-time monitoring data as a contributed software package in the R language that is fast becoming a very popular statistical computing platform. The package is able to fit, spatially and temporally predict large amounts of space-time data using three recently developed Bayesian models. The user is given control over many options regarding covariance function selection, distance calculation, prior selection and tuning of the implemented MCMC algorithms, although suitable defaults are provided. The package has many other attractive features such as on the fly transformations and an ability to spatially predict temporally aggregated summaries on the original scale, which saves the problem of storage when using MCMC methods for large datasets. A simulation example, with more than a million observations, and a real life data example are used to validate the underlying code and to illustrate the software capabilities.

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Section: A Practical Examplementioning

confidence: 99%

Section: A Practical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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spTimer: Spatio-Temporal Bayesian Modeling UsingR

Bakar¹,

Sahu²

2015

J. Stat. Soft.

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“…We only consider July and August as this is the high ozone season in the USA; detailed explanation of the data set are given in [29]. To illustrate the spatio-temporal DLM, we use the most important and statistically significant covariate, maximum temperature (cMAXTMP in • C) for modelling ozone data.…”

Section: Ozone Datamentioning

confidence: 99%

Hierarchical spatially varying coefficient and temporal dynamic process models using`spTDyn`

Bakar

Kokic

Jin

2015

Journal of Statistical Computation and Simulation

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Bayesian hierarchical spatio-temporal models are becoming increasingly important due to the increasing availability of space-time data in various domains. In this paper we develop a user friendly R package, spTDyn, for spatio-temporal modelling. It can be used to fit models with spatially varying and temporally dynamic coefficients. The former is used for modelling the spatially varying impact of explanatory variables on the response caused by spatial misalignment. This issue can arise when the covariates only vary over time, or when they are measured over a grid and hence do not match the locations of the response point-level data. The latter is to examine the temporally varying impact of explanatory variables in space-time data due, for example, to seasonality or other time-varying effects. The spTDyn package uses Markov chain Monte Carlo sampling written in C, which makes computations highly efficient, and the interface is written in R making these sophisticated modelling techniques easily accessible to statistical analysts. The models and software, and their advantages, are illustrated using temperature and ozone space-time data.

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“…Sahu and Baker [16] had worked on ozone pollution in New York state and had implemented a Bayesian model. Also Temiyasathit and al.…”

Section: Related Workmentioning

confidence: 99%

A Complex Mining Process about Air Quality

Juganaru-Mathieu¹,

Brambila²

2013

Procedings of the Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support

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In this paper we present a mining project about extracting knowledge from public documents concerning air pollution. Our collection contains annual reports about air quality, acid rains, climatological conditions in the large area of Mexico City. These reports contain reliable data and are generated by the Department of Environment, they are in a printable format (.pdf file) with number of pages, table of content, textual information, numerical information in tables, images. For a human being it is impossible to read the whole collection during a relatively short period (a few days or weeks) and understand the content of them. An automatic box of tools able to extract knowledge, to quick retrieve important term, to answer some exact questions about precise climate parameters would be an important help for lecturers.We will describe our project based upon a text and data mining process; the aims of the complex process are extract frequent temporal pattern, to extract association rules, to integrate also some information retrieval simple tools. In parallel, some data mining techniques will be used to detect the same types of data presented in every report and then to extract a numerical datamart containing climatological data structured by month, year, geographical area. The datamart will be analyzed also. The main steps of our mining process are: preparing documents (cleaning, removing images, table of contents, footnotes), transforming in structured document (in a XML format with a precise DTD), indexing, various algorithms and methods of mining, visualising results and validating knowledge.We think also that our methodology will concern also other collections of the same category : reliable data and informations presented in huge periodical reports.

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A comparison of Bayesian models for daily ozone concentration levels

Cited by 28 publications

References 13 publications

spTimer: Spatio-Temporal Bayesian Modeling UsingR

spTimer: Spatio-Temporal Bayesian Modeling UsingR

Hierarchical spatially varying coefficient and temporal dynamic process models using`spTDyn`

A Complex Mining Process about Air Quality

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A comparison of Bayesian models for daily ozone concentration levels

Cited by 28 publications

References 13 publications

spTimer: Spatio-Temporal Bayesian Modeling UsingR

spTimer: Spatio-Temporal Bayesian Modeling UsingR

Hierarchical spatially varying coefficient and temporal dynamic process models usingspTDyn

A Complex Mining Process about Air Quality

Contact Info

Product

Resources

About

Hierarchical spatially varying coefficient and temporal dynamic process models using`spTDyn`