Search results for "Bay"
showing 10 items of 1187 documents
Uncertainty related to climate change in the assessment of the DDF curve parameters
2017
In the context of climate change, the evaluation of the parameters of Depth-Duration-Frequency (DDF) curves has become a critical issue. Neglecting future rainfall variations could result in an overestimation/underestimation of DDF parameters and, consequently, of the design storm. In this study, uncertainty analysis was integrated into trend analysis to provide an estimate of trends that cannot actually be rigorously verified. A Bayesian procedure was suggested for the updating of DDF curve parameters and to evaluate the uncertainty related to their assessment. The proposed procedure also allowed identification of the years of a series that contributed most to the overall uncertainty relat…
Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas.
2012
Flood damage in urbanized watersheds may be assessed by combining the flood depth–damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth–damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth–damage curve…
Geographical spread of influenza incidence in Spain during the 2009 A(H1N1) pandemic wave and the two succeeding influenza seasons
2014
SUMMARYThe aim of this study was to monitor the spatio-temporal spread of influenza incidence in Spain during the 2009 pandemic and the following two influenza seasons 2010–2011 and 2011–2012 using a Bayesian Poisson mixed regression model; and implement this model of geographical analysis in the Spanish Influenza Surveillance System to obtain maps of influenza incidence for every week. In the pandemic wave the maps showed influenza activity spreading from west to east. The 2010–2011 influenza epidemic wave plotted a north-west/south-east pattern of spread. During the 2011–2012 season the spread of influenza was geographically heterogeneous. The most important source of variability in the m…
Modelling spatially sampled proportion processes
2018
Many ecological processes are measured as proportions and are spatially sampled. In all these cases the standard procedure has long been the transformation of proportional data with the arcsine square root or logit transformation, without considering the spatial correlation in any way. This paper presents a robust regression model to analyse this kind of data using a beta regression and including a spatially correlated term within the Bayesian framework. As a practical example, we apply the proposed approach to a spatio-temporally sampled fishery discard dataset.
Editorial: Methodological issues in psychology and social sciences research
2023
In recent decades, the classical methods of studying the psychometric properties of tests have advanced to evolve into new innovative statistical methods, which allows obtaining a new view of the data. In this sense, progress has been made in the use of item response theory and in the use of structural equation models. Likewise, classical statistical inference methods have advanced in hypothesis testing and Bayesian statistics is rapidly making its way both in experimental studies and in psychometric studies. The aim of this issue has been to contribute to the dissemination of new research methodologies in quantitative and qualitative analysis in Psychology, as well as to evaluate the effec…
Bayesian two-stage regression with parametric heteroscedasticity
2008
In this paper, we expand Kleibergen and Zivot's (2003) Bayesian two-stage (B2S) model by allowing for unequal variances. Our choice for modeling heteroscedasticity is a fully Bayesian parametric approach. As an application, we present a cross-country Cobb–Douglas production function estimation.
Enhancing TIR Image Resolution via Bayesian Smoothing for IRRISAT Irrigation Management Project
2013
Accurate estimation of physical quantities depends on the availability of High Resolution (HR) observations of the Earth surface. However, due to the unavoidable tradeoff between spatial and time resolution, the acquisition instants of HR data hardly coincides with those required by the estimation algorithms. A possible solution consists in constructing a synthetic HR observation at a given time k by exploiting Low Resolution (LR) and HR data acquired at different instants. In this work we recast this issue as a smoothing problem, thus focusing on cases in which observations acquired both before and after time k are available. The proposed approach is validated on a region of interest for t…
CorCast: A Distributed Architecture for Bayesian Epidemic Nowcasting and its Application to District-Level SARS-CoV-2 Infection Numbers in Germany
2021
Timely information on current infection numbers during an epidemic is of crucial importance for decision makers in politics, medicine, and businesses. As information about local infection risk can guide public policy as well as individual behavior, such as the wearing of personal protective equipment or voluntary social distancing, statistical models providing such insights should be transparent and reproducible as well as accurate. Fulfilling these requirements is drastically complicated by the large amounts of data generated during exponential growth of infection numbers, and by the complexity of common inference pipelines. Here, we present CorCast – a stable and scalable distributed arch…
Missing Value Estimation for Microarray Data by Bayesian Principal Component Analysis and Iterative Local Least Squares
2013
Published version of an article from the journal: Mathematical Problems in Engineering. Also available from Hindawi: http://dx.doi.org/10.1155/2013/162938 Missing values are prevalent in microarray data, they course negative influence on downstream microarray analyses, and thus they should be estimated from known values. We propose a BPCA-iLLS method, which is an integration of two commonly used missing value estimation methods-Bayesian principal component analysis (BPCA) and local least squares (LLS). The inferior row-average procedure in LLS is replaced with BPCA, and the least squares method is put into an iterative framework. Comparative result shows that the proposed method has obtaine…
Differential analysis of indicators of achievement in objective tests
2003
Often objective tests to evaluate student qualifications are used. However, the selection between different evaluation criteria is in the hands of teachers or evaluators preferences. This investigation offers, briefly, a revision of some strategies utilized for objective evaluation. We also present other alternative strategies to the most classical ones. Afterwards, the strategies of evaluation have been analized with data from 114 subjects, who were evaluated with a test. The results suggest there are two kinds of strategies, one where only students' performance are evaluated, and other, where the test and the students are evaluated at the same time. We also show the fail of concordance be…