IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3515-d925986.html
   My bibliography  Save this article

A More Flexible Asymmetric Exponential Modification of the Laplace Distribution with Applications for Chemical Concentration and Environment Data

Author

Listed:
  • Jimmy Reyes

    (Departamento de Matemáticas, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1270300, Chile)

  • Mario A. Rojas

    (Departamento de Matemáticas, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1270300, Chile)

  • Pedro L. Cortés

    (Departamento de Matemáticas, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1270300, Chile)

  • Jaime Arrué

    (Departamento de Matemáticas, Facultad de Ciencias Básicas, Universidad de Antofagasta, Antofagasta 1270300, Chile)

Abstract

In this work, a new family of distributions based on the Laplace distribution is introduced. We define this new family by its stochastic representation as the sum of two independent random variables, one with a Laplace distribution and the other with an exponential distribution. Using a Monte Carlo simulation study, the statistical performance of the estimators obtained by the moments and maximum likelihood methods were empirically evaluated. We studied the coverage probabilities and mean length of the confidence intervals of the corresponding parameters based on the asymptotic normality of these estimators. This simulation study reported a good statistical performance of these estimators. Fits were made to three real data sets with the new distribution, two related to chemical concentrations and one to the environment, comparing it with three similar distributions given in the literature. We have used information criteria for the selection of models. These results showed that the exponentially modified Laplace model can be an alternative distribution to model skewed data with high kurtosis. The new approach is a contribution to the tools of statisticians and various professionals interested in modeling data with high kurtosis.

Suggested Citation

  • Jimmy Reyes & Mario A. Rojas & Pedro L. Cortés & Jaime Arrué, 2022. "A More Flexible Asymmetric Exponential Modification of the Laplace Distribution with Applications for Chemical Concentration and Environment Data," Mathematics, MDPI, vol. 10(19), pages 1-19, September.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3515-:d:925986
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3515/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/19/3515/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ioana Gutu & Daniela Tatiana Agheorghiesei & Alexandru Tugui, 2023. "Assessment of a Workforce Sustainability Tool through Leadership and Digitalization," IJERPH, MDPI, vol. 20(2), pages 1-30, January.
    2. Daniela Andreini & Diego Rinallo & Giuseppe Pedeliento & Mara Bergamaschi, 2017. "Brands and Religion in the Secularized Marketplace and Workplace: Insights from the Case of an Italian Hospital Renamed After a Roman Catholic Pope," Journal of Business Ethics, Springer, vol. 141(3), pages 529-550, March.
    3. S. A. Abu Bakar & Saralees Nadarajah & Z. A. Absl Kamarul Adzhar, 2018. "Loss modeling using Burr mixtures," Empirical Economics, Springer, vol. 54(4), pages 1503-1516, June.
    4. Byrd, T. A. & Marshall, T. E., 1997. "Relating information technology investment to organizational performance: a causal model analysis," Omega, Elsevier, vol. 25(1), pages 43-56, February.
    5. Herbert Hoijtink & Meinte Vollema, 2003. "Contemporary Extensions of the Rasch Model," Quality & Quantity: International Journal of Methodology, Springer, vol. 37(3), pages 263-276, August.
    6. Jaewoong Yun, 2023. "Strategies for Improving the Sustainability of Fare-Free Policy for the Elderly through Preferences by Travel Modes," Sustainability, MDPI, vol. 15(20), pages 1-14, October.
    7. Malerba, Martino E. & Connolly, Sean R. & Heimann, Kirsten, 2015. "An experimentally validated nitrate–ammonium–phytoplankton model including effects of starvation length and ammonium inhibition on nitrate uptake," Ecological Modelling, Elsevier, vol. 317(C), pages 30-40.
    8. Aline Riboli Marasca & Maurício Scopel Hoffmann & Anelise Reis Gaya & Denise Ruschel Bandeira, 2021. "Subjective Well-Being and Psychopathology Symptoms: Mental Health Profiles and their Relations with Academic Achievement in Brazilian Children," Child Indicators Research, Springer;The International Society of Child Indicators (ISCI), vol. 14(3), pages 1121-1137, June.
    9. Friederike Paetz, 2016. "Persönlichkeitsmerkmale als Segmentierungsvariablen: Eine empirische Studie [Personality traits for market segmentation: An empirical study]," Schmalenbach Journal of Business Research, Springer, vol. 68(3), pages 279-306, August.
    10. Emre Demirkaya & Yang Feng & Pallavi Basu & Jinchi Lv, 2022. "Large-scale model selection in misspecified generalized linear models [Information theory and an extension of the maximum likelihood principle]," Biometrika, Biometrika Trust, vol. 109(1), pages 123-136.
    11. Rosbergen, Edward & Wedel, Michel & Pieters, Rik, 1997. "Analyzing visual attention tot repeated print advertising using scanpath theory," Research Report 97B32, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
    12. Eduardo Correia & Rodrigo Calili & José Francisco Pessanha & Maria Fatima Almeida, 2023. "Definition of Regulatory Targets for Electricity Non-Technical Losses: Proposition of an Automatic Model-Selection Technique for Panel Data Regressions," Energies, MDPI, vol. 16(6), pages 1-22, March.
    13. Nalan Basturk & Richard Paap & Dick van Dijk, 2008. "Structural Differences in Economic Growth," Tinbergen Institute Discussion Papers 08-085/4, Tinbergen Institute.
    14. Golob, Thomas F. & Regan, A C, 2002. "Trucking Industry Preferences for Driver Traveler Information Using Wireless Internet-enabled Devices," University of California Transportation Center, Working Papers qt40q8h6sf, University of California Transportation Center.
    15. Golob, Thomas F. & Regan, A C, 2003. "Traffic Congestion and Trucking Managers' Use of Automated Routing and Scheduling," University of California Transportation Center, Working Papers qt74z234n4, University of California Transportation Center.
    16. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    17. Bijmolt, T.H.A. & Wedel, M., 1996. "A Monte Carlo Evaluation of Maximum Likelihood Multidimensional Scaling Methods," Other publications TiSEM f72cc9d8-f370-43aa-a224-4, Tilburg University, School of Economics and Management.
    18. Omar N. Solinger & Woody van Olffen & Robert A. Roe & Joeri Hofmans, 2013. "On Becoming (Un)Committed: A Taxonomy and Test of Newcomer Onboarding Scenarios," Organization Science, INFORMS, vol. 24(6), pages 1640-1661, December.
    19. Naiara Escalante Mateos & Eider Goñi Palacios & Arantza Fernández-Zabala & Iratxe Antonio-Agirre, 2020. "Internal Structure, Reliability and Invariance across Gender Using the Multidimensional School Climate Scale PACE-33," IJERPH, MDPI, vol. 17(13), pages 1-24, July.
    20. Sarah Brown & William Greene & Mark N. Harris, 2014. "A New Formulation for Latent Class Models," Working Papers 2014006, The University of Sheffield, Department of Economics.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3515-:d:925986. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.