scholarly journals Real Data Analysis and Efficiency of the TEA Mantova Casale (Italy) Variable-speed Pumping Station

2014 ◽  
Vol 70 ◽  
pp. 248-255 ◽  
Author(s):  
C. Capponi ◽  
M. Ferrante ◽  
M. Pedroni ◽  
B. Brunone ◽  
S. Meniconi ◽  
...  
Keyword(s):  
Data Analysis ◽  
Real Data ◽  
Variable Speed ◽  
Pumping Station ◽  
Real Data Analysis

Accuracy and sensitivity of different Bayesian methods for genomic prediction using simulation and real data

2020 ◽  
Vol 19 (3) ◽  
Author(s):  
Saheb Foroutaifar
Keyword(s):  
Data Analysis ◽  
Bayesian Methods ◽  
Milk Fat ◽  
Prediction Accuracy ◽  
Real Data ◽  
Fat Percentage ◽  
Real Data Analysis ◽  
Wide Range ◽  
High Heritability ◽  
Qtl Effects

AbstractThe main objectives of this study were to compare the prediction accuracy of different Bayesian methods for traits with a wide range of genetic architecture using simulation and real data and to assess the sensitivity of these methods to the violation of their assumptions. For the simulation study, different scenarios were implemented based on two traits with low or high heritability and different numbers of QTL and the distribution of their effects. For real data analysis, a German Holstein dataset for milk fat percentage, milk yield, and somatic cell score was used. The simulation results showed that, with the exception of the Bayes R, the other methods were sensitive to changes in the number of QTLs and distribution of QTL effects. Having a distribution of QTL effects, similar to what different Bayesian methods assume for estimating marker effects, did not improve their prediction accuracy. The Bayes B method gave higher or equal accuracy rather than the rest. The real data analysis showed that similar to scenarios with a large number of QTLs in the simulation, there was no difference between the accuracies of the different methods for any of the traits.

Empirical Likelihood Testing for Longitudinal Varying Coefficient Models

2014 ◽  
Vol 518 ◽  
pp. 356-360
Author(s):  
Chang Qing Liu
Keyword(s):  
Data Analysis ◽  
Empirical Likelihood ◽  
Real Data ◽  
Likelihood Method ◽  
Varying Coefficient Models ◽  
Empirical Likelihood Method ◽  
Real Data Analysis ◽  
Varying Coefficient ◽  
Testing Method

By using the empirical likelihood method, a testing method is proposed for longitudinal varying coefficient models. Some simulations and a real data analysis are undertaken to investigate the power of the empirical likelihood based testing method.

scholarly journals Decomposition of Dynamical Signals into Jumps, Oscillatory Patterns, and Possible Outliers

Mathematics ◽  
2018 ◽  
Vol 6 (7) ◽  
pp. 124 ◽  
Author(s):  
Elena Barton ◽  
Basad Al-Sarray ◽  
Stéphane Chrétien ◽  
Kavya Jagan
Keyword(s):  
Time Series ◽  
Signal Processing ◽  
Data Analysis ◽  
Real Data ◽  
Real Data Analysis ◽  
Convex Optimisation ◽  
Dynamical Time

In this note, we present a component-wise algorithm combining several recent ideas from signal processing for simultaneous piecewise constants trend, seasonality, outliers, and noise decomposition of dynamical time series. Our approach is entirely based on convex optimisation, and our decomposition is guaranteed to be a global optimiser. We demonstrate the efficiency of the approach via simulations results and real data analysis.

Electric vehicles and power quality in low voltage networks: Real data analysis and modeling

2022 ◽  
Vol 305 ◽  
pp. 117718
Author(s):  
S. Torres ◽  
I. Durán ◽  
A. Marulanda ◽  
A. Pavas ◽  
J. Quirós-Tortós
Keyword(s):  
Data Analysis ◽  
Power Quality ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Real Data ◽  
Real Data Analysis

A reproducible robust likelihood approach to inference about marginal characteristics of binary data in paired settings

2019 ◽  
Vol 29 (1) ◽  
pp. 282-292
Author(s):  
Tsung-Shan Tsou
Keyword(s):  
Data Analysis ◽  
Binary Data ◽  
Real Data ◽  
Paired Data ◽  
Real Data Analysis ◽  
Marginal Homogeneity ◽  
New Strategy ◽  
Likelihood Approach ◽  
Robust Likelihood ◽  
Joint Probabilities

We introduce a robust likelihood approach to inference about marginal distributional characteristics for paired data without modeling correlation/joint probabilities. This method is reproducible in that it is applicable to paired settings with various sizes. The virtue of the new strategy is elucidated via testing marginal homogeneity in paired triplet scenario. We use simulations and real data analysis to demonstrate the merit of our robust likelihood methodology.

Influence of non-Gaussian noise on the effectiveness of cyclostationary analysis – Simulations and real data analysis

Measurement ◽  
2021 ◽  
Vol 171 ◽  
pp. 108814
Author(s):  
Jacek Wodecki ◽  
Anna Michalak ◽  
Agnieszka Wyłomańska ◽  
Radosław Zimroz
Keyword(s):  
Data Analysis ◽  
Gaussian Noise ◽  
Real Data ◽  
Real Data Analysis ◽  
Non Gaussian

An Archimedean Copula Family with Hyperbolic Cotangent Generator

2014 ◽  
Vol 22 (05) ◽  
pp. 761-768 ◽  
Author(s):  
Vadoud Najjari ◽  
Tomáš Bacigál ◽  
Hasan Bal
Keyword(s):  
Data Analysis ◽  
Real Data ◽  
Archimedean Copula ◽  
Temperature Extremes ◽  
Flood Peak ◽  
Real Data Analysis ◽  
Monthly Temperature ◽  
Analysis Application

Hyperbolic cotangent function is proposed as a generator of new Archimedean copula family and several properties are revealed. To show performance in real data analysis, application to modeling dependence between monthly temperature extremes as well as between flood peak and volume is given.

scholarly journals Opportunities for heat pumps adoption in existing buildings: real-data analysis and numerical simulation

2017 ◽  
Vol 134 ◽  
pp. 499-507 ◽  
Author(s):  
M. Jarre ◽  
M. Noussan ◽  
A. Poggio ◽  
M. Simonetti
Keyword(s):  
Numerical Simulation ◽  
Data Analysis ◽  
Real Data ◽  
Heat Pumps ◽  
Existing Buildings ◽  
Real Data Analysis
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