Search results for " Error."

Bayesian dynamic modeling of time series of dengue disease case counts

2017

The aim of this study is to model the association between weekly time series of dengue case counts and meteorological variables, in a high-incidence city of Colombia, applying Bayesian hierarchical dynamic generalized linear models over the period January 2008 to August 2015. Additionally, we evaluate the model’s short-term performance for predicting dengue cases. The methodology shows dynamic Poisson log link models including constant or time-varying coefficients for the meteorological variables. Calendar effects were modeled using constant or first- or second-order random walk time-varying coefficients. The meteorological variables were modeled using constant coefficients and first-order …

Balloon-borne match measurements of midlatitude cirrus clouds

2014

Observations of high supersaturations with respect to ice inside cirrus clouds with high ice water content (> 0.01 g kg−1) and high crystal number densities (> 1 cm−3) are challenging our understanding of cloud microphysics and of climate feedback processes in the upper troposphere. However, single measurements of a cloudy air mass provide only a snapshot from which the persistence of ice supersaturation cannot be judged. We introduce here the "cirrus match technique" to obtain information about the evolution of clouds and their saturation ratio. The aim of these coordinated balloon soundings is to analyze the same air mass twice. To this end the standard radiosonde equipment is complemente…

Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy

2007

Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …

Sensitivity of the SIMulation-EXtrapolation (SIMEX) methodology to mis-specification of the statistical properties of the measurement errors

2023

In hydrometeorological and environmental studies, it is common to seek relations between two variables (predictand and predictor), one of which (predictor) is affected by uncertainties. These errors unavoidably affect the results of the analyses by providing erroneous estimates of the parameters of the predictor-predictand model. A possible solution is represented by the SIMulation-EXtrapolation (SIMEX) methodology. This approach follows two steps: (1) perturbation of the predictor with increasing levels of uncertainties (multiples of the known error variance); and (2) finding a relation between the model's parameters and level of uncertainty, which allows their extrapolation to the error-f…

Revision of the Single-Channel Algorithm for Land Surface Temperature Retrieval From Landsat Thermal-Infrared Data

2009

This paper presents a revision, an update, and an extension of the generalized single-channel (SC) algorithm developed by Jimenez-Munoz and Sobrino (2003), which was particularized to the thermal-infrared (TIR) channel (band 6) located in the Landsat-5 Thematic Mapper (TM) sensor. The SC algorithm relies on the concept of atmospheric functions (AFs) which are dependent on atmospheric transmissivity and upwelling and downwelling atmospheric radiances. These AFs are fitted versus the atmospheric water vapor content for operational purposes. In this paper, we present updated fits using MODTRAN 4 radiative transfer code, and we also extend the application of the SC algorithm to the TIR channel …

Prior Precision Modulates the Minimization of Auditory Prediction Error

2019

International audience; The predictive coding model of perception proposes that successful representation of the perceptual world depends upon canceling out the discrepancy between prediction and sensory input (i.e., prediction error). Recent studies further suggest a distinction to be made between prediction error triggered by non-predicted stimuli of different prior precision (i.e., inverse variance). However, it is not fully understood how prediction error with different precision levels is minimized in the predictive process. Here, we conducted a magnetoencephalography (MEG) experiment which orthogonally manipulated prime-probe relation (for contextual precision) and stimulus repetition…

Explicitly Correlated Electrons in Molecules

2011

Rejection odds and rejection ratios: A proposal for statistical practice in testing hypotheses

2016

Much of science is (rightly or wrongly) driven by hypothesis testing. Even in situations where the hypothesis testing paradigm is correct, the common practice of basing inferences solely on p-values has been under intense criticism for over 50 years. We propose, as an alternative, the use of the odds of a correct rejection of the null hypothesis to incorrect rejection. Both pre-experimental versions (involving the power and Type I error) and post-experimental versions (depending on the actual data) are considered. Implementations are provided that range from depending only on the p-value to consideration of full Bayesian analysis. A surprise is that all implementations -- even the full Baye…

Commentary: Rational Adaptation in Lexical Prediction: The Influence of Prediction Strength

2021

An optical water type framework for selecting and blending retrievals from bio-optical algorithms in lakes and coastal waters.

2014

Bio-optical models are based on relationships between the spectral remote sensing reflectance and optical properties of in-water constituents. The wavelength range where this information can be exploited changes depending on the water characteristics. In low chlorophyll-a waters, the blue/green region of the spectrum is more sensitive to changes in chlorophyll-a concentration, whereas the red/NIR region becomes more important in turbid and/or eutrophic waters. In this work we present an approach to manage the shift from blue/green ratios to red/NIR-based chlorophyll-a algorithms for optically complex waters. Based on a combined in situ data set of coastal and inland waters, measures of over…