Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network

An analysis of air quality data is provided for the municipal area of Taranto characterized by high environmental risks, due to the massive presence of industrial sites with elevated environmental impact activities. The present study is focused on particulate matter as measured by PM10 concentrations. Preliminary analysis involved addressing several data problems, mainly: (i) an imputation techniques were considered to cope with the large number of missing data, due to both different working periods for groups of monitoring stations and occasional malfunction of PM10 sensors; (ii) due to the use of different validation techniques for each of the three monitoring networks, a calibration procedure was devised to allow for data comparability. Missing data imputation and calibration were addressed by three alternative procedures sharing a leave-one-out type mechanism and based on {\it ad hoc} exploratory tools and on the recursive Bayesian estimation and prediction of spatial linear mixed effects models. The three procedures are introduced by motivating issues and compared in terms of performance

Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network

An analysis of air quality data is provided for the municipal area of Taranto characterized by high environmental risks, due to the massive presence of industrial sites with elevated environmental impact activities. The present study is focused on particulate matter as measured by PM10 concentrations. Preliminary analysis involved addressing several data problems, mainly: (i) an imputation techniques were considered to cope with the large number of missing data, due to both different working periods for groups of monitoring stations and occasional malfunction of PM10 sensors; (ii) due to the use of different validation techniques for each of the three monitoring networks, a calibration procedure was devised to allow for data comparability. Missing data imputation and calibration were addressed by three alternative procedures sharing a leave-one-out type mechanism and based on {\it ad hoc} exploratory tools and on the recursive Bayesian estimation and prediction of spatial linear mixed effects models. The three procedures are introduced by motivating issues and compared in terms of performance

Generalized biodiversity assessment by Bayesian nested random effects models with spyke-and-slab priors

We analyze variations in alpha-diversity of benthic macroinvertebrate communities in an Italian lagoon system using Bayesian hierarchical models with nested random effects. Our aim is to understand how spatial scales influence microhabitat definition. Tsallis entropy measures diversity and spike-and-slab regression selects predictors

Spatio-temporal analysis of PM10 concentrations in the Taranto area

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HUBUNGAN ANTARA KEPRIBADIAN HARDINESS DENGAN KECENDERUNGAN PROBLEM FOCUS COPING PADA ANGGOTA POLISI

Stressor yang dialami setiap anggota Polisi dapat dimaknai berbeda tergantung oleh penilaian kognitif dari setiap individu. Kepribadian hardiness dan problem focus coping yang dimiliki individu bisa mempengaruhi seseorang dalam menghadapi pekerjaannya yang sulit, berbahaya dan stressfull. Tujuan penelitian ini untuk mengetahui apakah ada hubungan antara kepribadian hardiness dengan problem focus coping pada Polisi. Penelitian ini menggunakan skala hardiness yang memiliki indeks validitas 0,635-0,869 dan reliabilitas 0,952 dengan skala problem focus coping dengan validitas 0,698-0,964 dan reliabilitas 0,986 sebagai alat ukur. Sampel dalam penelitian ini sebanyak 100 orang anggota Polisi dengan teknik sampling yang digunakan ialah kuota sampling. Hasil dari penelitian ini adalah terdapat hubungan positif dan signifikan antara kepribadian hardiness dengan problem focus coping pada polisi (r = 0,664; p = 0,000). Sumbangan efektif kepribadian hardiness terhadap problem focus coping sebesar 44,1%. Hal tersebut menunjukkan bahwa semakin tinggi kepribadian hardiness maka semakin tinggi problem focus coping yang dimiliki

Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data

Presence-only data are referred to situations in which, given a censoring mechanism, a binary response can be observed only with respect to on outcome, usually called \textit{presence}. In this work we present a Bayesian approach to the problem of presence-only data based on a two levels scheme. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to the censoring and one due to the sampling. We propose a new formalization for the logistic model with presence-only data that allows further insight into inferential issues related to the model. We concentrate on the case of the linear logistic regression and, in order to make inference on the parameters of interest, we present a Markov Chain Monte Carlo algorithm with data augmentation that does not require the a priori knowledge of the population prevalence. A simulation study concerning 24,000 simulated datasets related to different scenarios is presented comparing our proposal to optimal benchmarks.Comment: Affiliations: Fabio Divino - Division of Physics, Computer Science and Mathematics, University of Molise Giovanna jona Lasinio and Natalia Golini - Department of Statistical Sciences, University of Rome "La Sapienza" Antti Penttinen - Department of Mathematics and Statistics, University of Jyv\"{a}skyl\"{a} CONTACT: [email protected], [email protected]

Reforms, labour market functioning and productivity dynamics: a sectoral analysis for Italy

Over the last two decades Italy registered notable improvements in the performance of the labour market both in terms of unemployment and participation. However, such improvements have been accompanied by a deterioration of productivity and competitiveness. This paper provides evidence in this respect evaluating to what extent labour market reforms might have influenced the poor productivity performance of the Italian economy over the period 1980-2008. Our results show that the increased flexibility in the use of temporary contracts has led to a lower productivity (level and to a lesser extent growth rate) in all sectors, with a higher impact on those sectors with a larger technological need for flexibility and a lower skill content. Moreover, the reforms had a negative impact on the productivity-enhancing reallocation by favoring a shift of employment towards low-productive industries. The negative effect of the reforms on the reallocative capacity is stronger in those industries with a higher flexibility need that are also the relatively lower productivity sectors in the period 1993-2008

Bayesian logistic regression for presence-only data

Presence-only data are referred to situations in which a censoring mechanism acts on a binary response which can be partially observed only with respect to one outcome, usually denoting the \textit{presence} of an attribute of interest. A typical example is the recording of species presence in ecological surveys. In this work a Bayesian approach to the analysis of presence-only data based on a two levels scheme is presented. A probability law and a case-control design are combined to handle the double source of uncertainty: one due to censoring and the other one due to sampling. In the paper, through the use of a stratified sampling design with non-overlapping strata, a new formulation of the logistic model for presence-only data is proposed. In particular, the logistic regression with linear predictor is considered. Estimation is carried out with a new Markov Chain Monte Carlo algorithm with data augmentation, which does not require the a priori knowledge of the population prevalence. The performance of the new algorithm is validated by means of extensive simulation experiments using three scenarios and comparison with optimal benchmarks. An application to data existing in literature is reported in order to discuss the model behaviour in real world situations together with the results of an original study on termites occurrences data