Beta Generalized Normal Distribution with an Application for SAR Image Processing
We introduce the beta generalized normal distribution which is obtained by compounding the beta and generalized normal [Nadarajah, S., A generalized normal distribution, \emph{Journal of Applied Statistics}. 32, 685--694, 2005] distributions. The new model includes as sub-models the beta normal, bet...
Gespeichert in:
Veröffentlicht in: | arXiv.org 2022-06 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | |
container_start_page | |
container_title | arXiv.org |
container_volume | |
creator | Cintra, R J Rêgo, L C Cordeiro, G M Nascimento, A D C |
description | We introduce the beta generalized normal distribution which is obtained by compounding the beta and generalized normal [Nadarajah, S., A generalized normal distribution, \emph{Journal of Applied Statistics}. 32, 685--694, 2005] distributions. The new model includes as sub-models the beta normal, beta Laplace, normal, and Laplace distributions. The shape of the new distribution is quite flexible, specially the skewness and the tail weights, due to two additional parameters. We obtain general expansions for the moments. The estimation of the parameters is investigated by maximum likelihood. We also proposed a random number generator for the new distribution. Actual synthetic aperture radar were analyzed and modeled after the new distribution. Results could outperform the \(\mathcal{G}^0\), \(\mathcal{K}\), and \(\Gamma\) distributions in several scenarios. |
doi_str_mv | 10.48550/arxiv.2206.01357 |
format | Article |
fullrecord | <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_2206_01357</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2673455420</sourcerecordid><originalsourceid>FETCH-LOGICAL-a520-b3f4471f3a71d520c0c14769a0e5a47c111fedd408bc952ded11c2e37a1660d53</originalsourceid><addsrcrecordid>eNotj11PwjAYhRsTEwnyA7yyidebb79WuERUJCF-cr-8tB2WjG22Q9Rf7xxeneTJycl5CLlgkMqxUnCN4ct_ppxDlgITSp-QAReCJWPJ-RkZxbgFAJ5prpQYkJcb1yKdu8oFLP2Ps_SxDjss6a2PbfDrfevrih58-06xotOmKb3BnhV1oG_TV7rY4cbR51AbF6OvNufktMAyutF_Dsnq_m41e0iWT_PFbLpMUHFI1qKQUrNCoGa2AwYMkzqbIDiFUhvGWOGslTBem4ni1lnGDHdCI8sysEoMyeVxtvfNm-B3GL7zP--89-4aV8dGE-qPvYttvq33oeo-5Z2-kEpJDuIXTWxaQA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2673455420</pqid></control><display><type>article</type><title>Beta Generalized Normal Distribution with an Application for SAR Image Processing</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Cintra, R J ; Rêgo, L C ; Cordeiro, G M ; Nascimento, A D C</creator><creatorcontrib>Cintra, R J ; Rêgo, L C ; Cordeiro, G M ; Nascimento, A D C</creatorcontrib><description>We introduce the beta generalized normal distribution which is obtained by compounding the beta and generalized normal [Nadarajah, S., A generalized normal distribution, \emph{Journal of Applied Statistics}. 32, 685--694, 2005] distributions. The new model includes as sub-models the beta normal, beta Laplace, normal, and Laplace distributions. The shape of the new distribution is quite flexible, specially the skewness and the tail weights, due to two additional parameters. We obtain general expansions for the moments. The estimation of the parameters is investigated by maximum likelihood. We also proposed a random number generator for the new distribution. Actual synthetic aperture radar were analyzed and modeled after the new distribution. Results could outperform the \(\mathcal{G}^0\), \(\mathcal{K}\), and \(\Gamma\) distributions in several scenarios.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.2206.01357</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Applied statistics ; Image processing ; Mathematical models ; Mathematics - Probability ; Mathematics - Statistics Theory ; Normal distribution ; Parameters ; Physics - Data Analysis, Statistics and Probability ; Random numbers ; Statistics - Theory ; Synthetic aperture radar</subject><ispartof>arXiv.org, 2022-06</ispartof><rights>2022. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.1080/02331888.2012.748776$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.48550/arXiv.2206.01357$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Cintra, R J</creatorcontrib><creatorcontrib>Rêgo, L C</creatorcontrib><creatorcontrib>Cordeiro, G M</creatorcontrib><creatorcontrib>Nascimento, A D C</creatorcontrib><title>Beta Generalized Normal Distribution with an Application for SAR Image Processing</title><title>arXiv.org</title><description>We introduce the beta generalized normal distribution which is obtained by compounding the beta and generalized normal [Nadarajah, S., A generalized normal distribution, \emph{Journal of Applied Statistics}. 32, 685--694, 2005] distributions. The new model includes as sub-models the beta normal, beta Laplace, normal, and Laplace distributions. The shape of the new distribution is quite flexible, specially the skewness and the tail weights, due to two additional parameters. We obtain general expansions for the moments. The estimation of the parameters is investigated by maximum likelihood. We also proposed a random number generator for the new distribution. Actual synthetic aperture radar were analyzed and modeled after the new distribution. Results could outperform the \(\mathcal{G}^0\), \(\mathcal{K}\), and \(\Gamma\) distributions in several scenarios.</description><subject>Applied statistics</subject><subject>Image processing</subject><subject>Mathematical models</subject><subject>Mathematics - Probability</subject><subject>Mathematics - Statistics Theory</subject><subject>Normal distribution</subject><subject>Parameters</subject><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Random numbers</subject><subject>Statistics - Theory</subject><subject>Synthetic aperture radar</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj11PwjAYhRsTEwnyA7yyidebb79WuERUJCF-cr-8tB2WjG22Q9Rf7xxeneTJycl5CLlgkMqxUnCN4ct_ppxDlgITSp-QAReCJWPJ-RkZxbgFAJ5prpQYkJcb1yKdu8oFLP2Ps_SxDjss6a2PbfDrfevrih58-06xotOmKb3BnhV1oG_TV7rY4cbR51AbF6OvNufktMAyutF_Dsnq_m41e0iWT_PFbLpMUHFI1qKQUrNCoGa2AwYMkzqbIDiFUhvGWOGslTBem4ni1lnGDHdCI8sysEoMyeVxtvfNm-B3GL7zP--89-4aV8dGE-qPvYttvq33oeo-5Z2-kEpJDuIXTWxaQA</recordid><startdate>20220603</startdate><enddate>20220603</enddate><creator>Cintra, R J</creator><creator>Rêgo, L C</creator><creator>Cordeiro, G M</creator><creator>Nascimento, A D C</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>AKZ</scope><scope>EPD</scope><scope>GOX</scope></search><sort><creationdate>20220603</creationdate><title>Beta Generalized Normal Distribution with an Application for SAR Image Processing</title><author>Cintra, R J ; Rêgo, L C ; Cordeiro, G M ; Nascimento, A D C</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a520-b3f4471f3a71d520c0c14769a0e5a47c111fedd408bc952ded11c2e37a1660d53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Applied statistics</topic><topic>Image processing</topic><topic>Mathematical models</topic><topic>Mathematics - Probability</topic><topic>Mathematics - Statistics Theory</topic><topic>Normal distribution</topic><topic>Parameters</topic><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Random numbers</topic><topic>Statistics - Theory</topic><topic>Synthetic aperture radar</topic><toplevel>online_resources</toplevel><creatorcontrib>Cintra, R J</creatorcontrib><creatorcontrib>Rêgo, L C</creatorcontrib><creatorcontrib>Cordeiro, G M</creatorcontrib><creatorcontrib>Nascimento, A D C</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Mathematics</collection><collection>arXiv Statistics</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cintra, R J</au><au>Rêgo, L C</au><au>Cordeiro, G M</au><au>Nascimento, A D C</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Beta Generalized Normal Distribution with an Application for SAR Image Processing</atitle><jtitle>arXiv.org</jtitle><date>2022-06-03</date><risdate>2022</risdate><eissn>2331-8422</eissn><abstract>We introduce the beta generalized normal distribution which is obtained by compounding the beta and generalized normal [Nadarajah, S., A generalized normal distribution, \emph{Journal of Applied Statistics}. 32, 685--694, 2005] distributions. The new model includes as sub-models the beta normal, beta Laplace, normal, and Laplace distributions. The shape of the new distribution is quite flexible, specially the skewness and the tail weights, due to two additional parameters. We obtain general expansions for the moments. The estimation of the parameters is investigated by maximum likelihood. We also proposed a random number generator for the new distribution. Actual synthetic aperture radar were analyzed and modeled after the new distribution. Results could outperform the \(\mathcal{G}^0\), \(\mathcal{K}\), and \(\Gamma\) distributions in several scenarios.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.2206.01357</doi><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | EISSN: 2331-8422 |
ispartof | arXiv.org, 2022-06 |
issn | 2331-8422 |
language | eng |
recordid | cdi_arxiv_primary_2206_01357 |
source | arXiv.org; Free E- Journals |
subjects | Applied statistics Image processing Mathematical models Mathematics - Probability Mathematics - Statistics Theory Normal distribution Parameters Physics - Data Analysis, Statistics and Probability Random numbers Statistics - Theory Synthetic aperture radar |
title | Beta Generalized Normal Distribution with an Application for SAR Image Processing |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T07%3A11%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Beta%20Generalized%20Normal%20Distribution%20with%20an%20Application%20for%20SAR%20Image%20Processing&rft.jtitle=arXiv.org&rft.au=Cintra,%20R%20J&rft.date=2022-06-03&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.2206.01357&rft_dat=%3Cproquest_arxiv%3E2673455420%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2673455420&rft_id=info:pmid/&rfr_iscdi=true |