Journal of Environmental Statistics http://www.jenvstat.org/rss Tue, 26 Mar 2019 02:43:39 GMT Tue, 26 Mar 2019 02:43:39 GMT Most recent publications from the Journal of Environmental Statistics Modeling of Dengue Fever Death Counts Using Hidden Markov Model http://www.jenvstat.org/v08/i09/paper Vol. 8, Issue 9, Sep 2018

Abstract:

We explore the use of Poisson-hidden Markov model to describe an overdispersed data on monthly death counts due to Dengue fever. Independent Poisson mixture models of various components and stationary Poisson hidden Markov models of different states are fitted and the performance of each model is judged using model selection criteria. The sequence of hidden states are estimated based on the best fitted model. The method can be applied in identifying environmental factors affecting a stochastic process.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i09 Joshni George, Seemon Thomas
On Identifying the Probability Distribution of Monthly Maximum Temperature of Two Coastal Stations in Bangladesh http://www.jenvstat.org/v08/i08/paper Vol. 8, Issue 8, Sep 2018

Abstract:

Rising temperature in the atmosphere causes sea level rise and affects low lying coastal areas and deltas of the world. The last decade of the twentieth century was globally the hottest since the beginning of worldwide temperature measurement during the nineteenth century. Many PDFs have been proposed in recent past, but in present study Weibull, Lognormal, Gamma, GEV, etc are used to describe the characteristics of maximum temperature. This paper attempts to determine the best fitted probability distribution of monthly maximum temperature. To identify the appropriate probability distribution of the observed data, this paper considers a data set on the monthly maximum temperature of two coastal stations (Cox’s Bazar and Patuakhali) over the period January, 1971 to November, 2015 and January, 1973 to November, 2015 respectively. To check the accuracy of the predicted data using theoretical probability distributions the goodness-of-fit criteria like KS, R², χ2, and RMSE were used in this paper. According to the goodness-of-fit criteria and from the graphical comparisons it can be said that Generalized Skew Logistic distribution (GSL) provides the best fit for the observed monthly maximum temperature data of Cox’s Bazar and Weibull (W) gives the best fit for Patuakhali among the probability distributions considered in this paper.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i08 Md. Moyazzem Hossain, Faruq Abdulla, Gazi Mahmud Alam
Prediction Limits for the Mean of a Sample from a Lognormal Distribution: Uncensored and Censored Cases http://www.jenvstat.org/v08/i07/paper Vol. 8, Issue 7, Sep 2018

Abstract:

For some regulatory purposes, it is desired to compare average on-site pollution concentrations in a narrowly defined geographic area with a large collection of background measurements. An approach to this problem is to treat this as a statistical prediction for the mean of a future sample based on a background sample. In this article, assuming lognormality, a fiducial approach is described for constructing prediction limits for the mean of a sample when the background sample is uncensored or censored. The fiducial prediction limits are evaluated with respect to coverage probabilities, and are compared with those based on another approximate method. Monte Carlo simulation studies for the uncensored case indicate that the fiducial methods are accurate and practically exact even for small samples, and they are very satisfactory for the censored case. Algorithms for computation of confidence limits are provided. The methods are illustrated using two real data sets.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i07 Md. Sazib Hasan, K. Krishnamoorthy
A simulation comparison of estimators of spatial covariance parameters and associated bootstrap percentiles http://www.jenvstat.org/v08/i06/paper Vol. 8, Issue 6, Sep 2018

Abstract:

A simulation study is implemented to study estimators of the covariance structure of a stationary Gaussian spatial process and a spatial process with t-distributed margins. The estimators compared are Gaussian restricted maximum likelihood (REML) and curve-fitting by ordinary least squares and by the nonparametric Shapiro-Botha approach. Processes with Matérn covariance functions are considered and the parameters estimated are the nugget, partial sill and practical range. Both parametric and nonparametric bootstrap distributions of the estimators are computed and compared to the true marginal distributions of the estimators.

Gaussian REML is the estimator of choice for both Gaussian and t-distributed data and all choices of the Matérn covariance structure. However, accurate estimation of the Matérn shape parameter is critical to achieving a good fit while this does not affect the Shapiro-Botha estimator. The parametric bootstrap performed well for all estimators although it tended to be biased downward. It was slightly better than the nonparametric bootstrap for Gaussian data, equivalent to it for t-distributed data and worse overall for the Shapiro-Botha estimates.

A numerical example, obtained from environmental monitoring, is included to illustrate the application of the methods and the bootstrap.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i06 Raquel Menezes, Gabrielle Kelly
Statistical Models for Evaluating Water Pollution: The Case of Asejire and Eleyele Reservoirs in Nigeria http://www.jenvstat.org/v08/i05/paper Vol. 8, Issue 5, Sep 2018

Abstract:

Water pollution is a major environmental problem due to rapid population growth that over exploit and pollute the water resources. In this work the physico-chemical study of Asejire and Eleyele reservoirs are carried out to examine the water pollution levels. Eleyele and Asejire reservoirs are the two major sources of pipe-borne water in Ibadan with a population of about four million people. Water samples were collected from both sites from January 2003-December 2007 and analysed for 13 physico-chemical parameters. The data were subjected to Principal Component Analysis (PCA) to define the parameters responsible for the main variability in water pollution. The PCA produces 5 significant main components explaining 66.6% and 69.8% variance in Asejire and Eleyele reservoir, respectively. Generalized Linear Model (GLM) is applied to study the variability in turbidity level which shows that four parameters in each reservoir are important to explain the turbidity variation. Also many parameters in Asejire lie within the SON and WHO permissible limits while in Eleyele reservoir many parameters lie out. This therefore is an indication that water in Eleyele reservoir is more polluted than in Asejire reservoir.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i05 K. O. Obisesan, Privatus Christopher
Hints of latent drivers investigating university student performance http://www.jenvstat.org/v08/i04/paper Vol. 8, Issue 4, Sep 2018

Abstract:

Job market, nowadays, asks for higher and higher skills and competences. Therefore, also the measurement and assessment of the university students performance are crucial issues for policy makers. Although the scientific literature provides several papers investigating the main determinants of university student performance, often results are very different, and they seem to hold just in very specific contexts. This paper aims to contribute to the international literature, focusing on the role of student specific characteristics, supporting the idea that unobservable variables (such as motivation, aptitudes or abilities) should be more investigated.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i04 Giovanni Boscaino, Giada Adelfio
Likelihood-based detection of cluster centers for Neyman–Scott point processes http://www.jenvstat.org/v08/i03/paper Vol. 8, Issue 3, Sep 2018

Abstract:

This study deals with the problem of estimating the unobservable cluster centers for a special type of Neyman"Scott point processes, in which the cluster sizes (numbers of members in each cluster) are distributed according to the Poisson distribution. The key point of the solution is the conversion among different forms of conditional intensities, λ(t | ·)dt = P{N[t,t + dt) = 1 | ·} = E[N[t,t + dt) | ·], where · represents a σ-algebra generated by some information from the process N. Some recursive formulae associated with the filtering gain (information gain represented by the ratio of the likelihood of the point process when we know more information to the likelihood when we know less) are derived. These recursive equations can be solved numerically by using Monte Carlo integration. The proposed method is illustrated by two simulation experiments, a purely temporal and a multi-type spatiotemporal case.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i03 Jiancang Zhuang
Rater Classification by Means of Set-theoretic Methods Applied to Forestry Data http://www.jenvstat.org/v08/i02/paper Vol. 8, Issue 2, Sep 2018

Abstract:

We consider a situation where r raters select subsets from a set of n items by marking them by ‘0’ or ‘1’, as in classification problems, approval voting and in general subset voting. The number r of raters is small in comparison to the number n of items. We intend to classify the raters, to understand their behavior and to go beyond the possibilities of classical statistical methods such as Fleiss’ kappa, cluster analysis or latent class analysis. We use a non-parametric set-theoretic approach, which is natural for the given dichotomous setting. We recommend the determination of a set-theoretic mean, the Vorob’ev expectation, to play a role similar to the classical mean of a sample. In particular, we use distances of the raters’ subsets from the mean as characteristics of the individual raters. Furthermore, we introduce a new measure of conformity of a given rater with all others, characterizing the extent to which the rater deviates from the whole group of raters. We demonstrate the use of these methods in a case study, where the raters are forest managers and the items are trees in a forest thinning experiment. Our aim is to contribute to an understanding of the psychological processes involved, when forest managers mark trees for forest operations.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i02 Andreas Wünsche, Dietrich Stoyan, Arne Pommerening
Introduction to Special Issue on Novel or Unusual Ideas in Environmental Statistics http://www.jenvstat.org/v08/i01/paper Vol. 8, Issue 1, Sep 2018

Abstract:

Innovative ideas are often unfairly rejected by journals during the publication process, in favor of more standard, mainstream articles. Brief comments on this problem, which motivated this special issue, are given.

]]>
Sat, 15 Sep 2018 07:00:00 GMT /v08/i01 Rick Paik Schoenberg
A Bayesian Model-Based Approach for Determining Multivariate Tolerable Regions http://www.jenvstat.org/v07/i06/paper Vol. 7, Issue 6, Nov 2015

Abstract:

A crucial concern of toxicologists is to determine an acceptable exposure level(s) to a hazardous substance(s). Often lab experiments produce data featuring multiple hazards and multiple outcome measures. The current practice evaluates each hazard and outcome combination separately, which leads to multiple statistical tests that suffer from inflated Type I error rates. This paper introduces a Bayesian model-based approach for analyzing data of similar nature. This approach is dimension-preserving in that it permits simultaneous quantification of an acceptable exposure level among multiple hazards. Furthermore, we introduce the concept of significance probabilities to assess the importance of the outcomes in determining an acceptable exposure level. The proposed methodology is motivated and illustrated through analyzing the dataset from a rodent study of pesticides on neurotoxicity conducted by Moser et al. (2005).

]]>
Wed, 18 Nov 2015 08:00:00 GMT /v07/i06 J. Paul Brooks, Xi Chen, Epiphanie Nyirabahizi, Edward L. Boone
Regression of Algae Biomass over Variables with Disjoint Spatial Support http://www.jenvstat.org/v07/i05/paper Vol. 7, Issue 5, Sep 2015

Abstract:

Algae biomass in California watersheds are impacted by several covariates including atmospheric nitrogen, watershed nutrients, land-use variables and physical-habitat variables. However, with several different agencies collecting and reporting data on both biomass response variables and subsets of all potential predictors, the result is several variables contained within multiple datasets, each compiled with data disjoint both in space and time from the other datasets. In this paper, the authors discuss a spatial statistical technique for forecasting values for each response and predictor over a shared spatial support and then using weighted standardized regression to identify which predictive variables are most important in explaining variability in algae biomass levels. Results will indicate that algae biomass levels are consistently correlated with the following: N03, N0x, Total Nitrogen and some land-use variables, while physical-habitat variables and atmospheric nitrogen are less successful predictors of algae biomass population density.

]]>
Tue, 29 Sep 2015 07:00:00 GMT /v07/i05 Kali Chowdhury, Jane Giamporcaro, Anthony Rambone, Atousa Karimi, Kevin Nichols, Elena Trevino, Calvin Pham
Parametric Approaches for Estimating the Number of Species of Water Birds of Bangladesh http://www.jenvstat.org/v07/i04/paper Vol. 7, Issue 4, Sep 2015

Abstract:

Water birds are important indicators of ecological change in wetland ecosystems. By estimating the species richness of waterbirds, the changes in the environmental behaviour of Bangladesh may be assumed substantially. The leading idea of this paper is to estimate the number of species of water birds of Bangladesh. Many parametric models are used for the estimation of species richness now-a-days. We fit four parametric models to the data of water birds and compared them on the basis of AIC values and standard errors. Among these models, two-mixed exponential mixed Poisson model has proven to be the best fit model.

]]>
Tue, 29 Sep 2015 07:00:00 GMT /v07/i04 Farhana Sadia, Marzana Chowdhury, Syed S. Hossain
Pairwise Interaction Point Processes for Modelling Bivariate Spatial Point Patterns in the Presence of Interaction Uncertainty http://www.jenvstat.org/v07/i03/paper Vol. 7, Issue 3, Sep 2015

Abstract:

Current ecological research seeks to understand the mechanisms that sustain biodiver- sity and allow a large number of species to coexist. Coexistence concerns inter-individual interactions. Consequently, there is an interest in identifying and quantifying interactions within and between species as reflected in the spatial pattern formed by the individuals. This study analyses the spatial pattern formed by the locations of plants in a commu- nity with high biodiversity from Western Australia. We fit a pairwise interaction Gibbs marked point process to the data using a Bayesian approach and quantify the inhibitory interactions within and between the two species. We quantitatively discriminate between competing models corresponding to different inter-specific and intraspecific interactions via posterior model probabilities. The analysis provides evidence that the intraspecific interactions for the two species of the genus Banksia are generally similar to those between the two species providing some evidence for mechanisms that sustain biodiversity.

]]>
Tue, 29 Sep 2015 07:00:00 GMT /v07/i03 Janine Bärbel Illian, Glenna F. Nightingale, Ruth King
Analysis of Spatial Heterogeneity Using Power Law http://www.jenvstat.org/v07/i02/paper Vol. 7, Issue 2, Sep 2015

Abstract:

We tried to find out the spatial heterogeneity of plant species using power law. A field study was conducted on four grasslands each grazed by a single cow. Grasslands were Dactylis glomerata L. dominated grassland without feces (DgF-), Dactylis glomerata L. dominated grassland with feces (DgF+), Veronica arvensis dominated grassland without feces (VaF-) and Veronica arvensis dominated grassland with feces (VaF+). In each grassland, a 50 m line transect was drawn. Each of the four grasslands was surveyed by placing (=100) equal spaced large quadrats (L-quadrats) along the line transect. Each L-quadrats was divided into equal spaced small quadrats (S-quadrats). For each S-quadrat the occurrence of all plant species were recorded. Using the frequency distribution table these data were summarized. The percentage of S-quadrats containing a given species and the variance of each species were estimated. Using the power law the spatial heterogeneity of each species together with community heterogeneity were calculated. We compared degree of heterogeneity index calculated from beta-binomial distribution and from the regression analysis using the power law. The heterogeneity index from the regression analysis was found to be superior to that of the beta-binomial distribution with respect to evaluate the spatial heterogeneity of each species. The per L-quadrat diversity for DgF+ and VaF+ were higher compared to those of the DgF- and VaF-.

]]>
Tue, 29 Sep 2015 07:00:00 GMT /v07/i02 Mikinori Tsuiki, Tamanna Islam
A Markov Approach On Pattern of Rainfall Distribution http://www.jenvstat.org/v07/i01/paper Vol. 7, Issue 1, Sep 2015

Abstract:

A three-state Markov chain was employed to examine the pattern and distribution of daily rainfall in Uyo metropolis of Nigeria using 15 years (1995-2009) rainfall data obtained from University of Uyo meteorological centre. The Chi-square (χ2) and WS test statistics were used to test the goodness of fit of Markov chain to the data. Each year was divided into three different periods viz: Pre-monsoon (Jan 1 ' March 31), Monsoon (April ' Sept. 30) and Post-monsoon (Oct. 1 ' Dec. 31). A day was regarded as a dry day if the rainfall was not more than 2.50mm, as a wet day if the rainfall was between 2.51mm to 5.00mm and as a rainy day if rainfall was above 5.00mm. Based on the three conditions of rainfall (dry, wet and rainy) and the statistical techniques applied, it was observed that for the Pre-Monsoon period with Weather Cycle (WC) of 12 days in Uyo metropolis, the expected length (duration) of dry, wet and rainy days were 10 days, 1 day and 1 day respectively. Also, for the Monsoon period with a WC of 5 days, the expected length of dry, wet and rainy days were 2 days, 1 day and 2days while for the Post-Monsoon period with a WC of 8 days, the expected length of dry, wet and rainy days were 6 days, 1 day and 2days respectively.

]]>
Tue, 29 Sep 2015 07:00:00 GMT /v07/i01 W. B. Yahya, M. A. Raheem, K. O. Obisesan