Fitting asymmetric bimodal data with selected distributions

Andrade, Bernardo Borba de; Rathie, Pushpa N.

doi:10.1080/00949655.2016.1157180

Cited by 10 publications

(5 citation statements)

References 23 publications

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“…Following Andrade and Rathie (2016), we calculate three summary statistics in fitting parametric distributions to histograms. Bayesian Information Criterion (BIC) is obtained from the likelihood function when fitting each parametric distribution; see Table C.1.…”

Section: Table 1: Bie Panel Representativenessmentioning

confidence: 99%

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Meyer

Parker

Sheng

2021

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The authors thank Longji Li, Dingqian Liu, and Brian Prescott for excellent research assistance and the Federal Reserve Bank of Atlanta for its ongoing support of the Economic Survey Research Center and the Business Inflation Expectations survey. They also thank Jose Maria Barrero (discussant), Carola Binder (discussant), Wojtek Charemza, and participants at the Philadelphia Fed Conference on "Real-Time Data Analysis, Methods and Applications" and the 21st IWH-CIREQ-GW Macroeconometric Workshop for helpful comments. Bernie Andrade kindly provided us the R code to fit the bimodal distribution. The views expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors' responsibility. All results have been reviewed to ensure that no confidential information was disclosed.

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Section: Table 1: Bie Panel Representativenessmentioning

confidence: 99%

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Meyer

Parker

Sheng

2021

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“…The bimodal extended generalized gamma (BEGG) [ 29 ], the Rathie-Swamee (RS) (RS is also known to be a modified version of generalized logistic) [ 11 , 12 , 13 ], the exponentiated sinh Cauchy (ESC) [ 10 ] and the alpha-skew Laplace (ASL) [ 24 ] distributions are used to fit the data and make a comparison between them and ABEP. There are many different distributions that have been proposed; however, using an explicit expression for CDF would be preferred to fit the data.…”

Section: Real Data Illustrationmentioning

confidence: 99%

“…The different bimodal and skew distributions have been proposed over the last decade to construct flexible distributions. The proposed distributions are in [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] and references therein via using different generating techniques [ 27 ] to get a probability density function (PDF). In these distributions,

-skew form of gamma distribution on the real line was proposed by [ 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Asymmetric Bimodal Exponential Power Distribution on the Real Line

Çankaya

2018

Entropy

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Abstract:The asymmetric bimodal exponential power (ABEP) distribution is an extension of the generalized gamma distribution to the real line via adding two parameters that fit the shape of peakedness in bimodality on the real line. The special values of peakedness parameters of the distribution are a combination of half Laplace and half normal distributions on the real line. The distribution has two parameters fitting the height of bimodality, so capacity of bimodality is enhanced by using these parameters. Adding a skewness parameter is considered to model asymmetry in data. The location-scale form of this distribution is proposed. The Fisher information matrix of these parameters in ABEP is obtained explicitly. Properties of ABEP are examined. Real data examples are given to illustrate the modelling capacity of ABEP. The replicated artificial data from maximum likelihood estimates of parameters of ABEP and other distributions having an algorithm for artificial data generation procedure are provided to test the similarity with real data. A brief simulation study is presented.

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“…"Real Value" [64] and "EU-SysFlex" [32] are two large DSM studies with the Irish TSO as a participant. "RealValue" [64] ran from 2015 to 2018 and deployed Smart Electric Thermal Storage (SETS) in 1250 homes in Germany, Latvia, and Ireland (volumes per country are unspecified), demonstrating scalability through modelling and virtual simulation [65,66] and finding that SETS can meet householders' space and water heating needs in a low-cost and energy-efficient manner whilst enabling residential storage heaters to connect on a cloud aggregation platform to charge based on grid constraints and market price signals. "EU-SysFlex" [32], carried out between 2018 and 2020, trialled "Residential Service Provision" through the "Power Off and Save project" [32,67], in which over 1400 households in Ireland were engaged to reduce their electricity consumption at peak times.…”

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confidence: 99%

Harnessing Curtailed Wind-Generated Electricity via Electrical Water Heating Aggregation to Alleviate Energy Poverty: A Use Case in Ireland

Ahern,

Oliver,

Norton

2024

Sustainability

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Ireland experiences high energy poverty rates alongside surplus wind energy resources. With 77% of Irish households equipped with electrical immersion heaters for domestic hot water (DHW) generation, this study proposes an Electrical Water Heating Aggregation (EWHA) scheme. The scheme allocates surplus wind-generated electricity to provide DHW to fuel-poor households, thereby alleviating energy poverty through harnessing curtailed wind energy. Through a developed wind-generated electricity allocation model and half-hourly data analysis for a weather year, this research assesses the feasibility and economic viability of the EWHA scheme, focusing on the householder as the primary benefactor from the scheme (as opposed to ancillary grid service provision). The results suggest an optimal aggregation size where maximum curtailment and carbon offset coincide with maximum benefits for participants. The findings indicate that fuel-poor households in Ireland could receive a full DHW tank every three weeks using surplus wind energy, harnessing 89% of overnight curtailed wind energy and offsetting 33 MtCO2 annually. Moreover, the scheme could potentially save the Irish state approximately EUR 4 million by 2030, increasing to EUR 11 million by 2050, in carbon costs. Overall, this research demonstrates the potential of EWHA schemes to alleviate energy poverty, optimise wind energy utilisation, and contribute significantly to carbon emission reduction targets.

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Fitting asymmetric bimodal data with selected distributions

Cited by 10 publications

References 23 publications

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Asymmetric Bimodal Exponential Power Distribution on the Real Line

Harnessing Curtailed Wind-Generated Electricity via Electrical Water Heating Aggregation to Alleviate Energy Poverty: A Use Case in Ireland

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