Statistical model for analizing negative variables with application to compression test on concrete
ABSTRACT: In some areas of knowledge, we can find phenomena represented by negative variables (R# ) ; having a statistical model is crucial to describe the phenomenon and explain it using other variables. This paper proposes a regression model to analyze negative random variables using the reflected...
- Autores:
-
Hernández Barajas, Freddy
Urrea Montoya, Amylkar
Patiño Rodríguez, Carmen Elena
Usuga Manco, Olga Cecilia
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2022
- Institución:
- Universidad de Antioquia
- Repositorio:
- Repositorio UdeA
- Idioma:
- eng
- OAI Identifier:
- oai:bibliotecadigital.udea.edu.co:10495/35280
- Acceso en línea:
- https://hdl.handle.net/10495/35280
- Palabra clave:
- Datos estadísticos
Statistical data
Estimación de parámetros
Parameter estimation
Análisis de regresión
Regression analysis
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/2.5/co/
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| dc.title.spa.fl_str_mv |
Statistical model for analizing negative variables with application to compression test on concrete |
| dc.title.translated.spa.fl_str_mv |
Modelo estadístico para el análisis de variables negativas con aplicación a pruebas de contracción en concreto |
| title |
Statistical model for analizing negative variables with application to compression test on concrete |
| spellingShingle |
Statistical model for analizing negative variables with application to compression test on concrete Datos estadísticos Statistical data Estimación de parámetros Parameter estimation Análisis de regresión Regression analysis |
| title_short |
Statistical model for analizing negative variables with application to compression test on concrete |
| title_full |
Statistical model for analizing negative variables with application to compression test on concrete |
| title_fullStr |
Statistical model for analizing negative variables with application to compression test on concrete |
| title_full_unstemmed |
Statistical model for analizing negative variables with application to compression test on concrete |
| title_sort |
Statistical model for analizing negative variables with application to compression test on concrete |
| dc.creator.fl_str_mv |
Hernández Barajas, Freddy Urrea Montoya, Amylkar Patiño Rodríguez, Carmen Elena Usuga Manco, Olga Cecilia |
| dc.contributor.author.none.fl_str_mv |
Hernández Barajas, Freddy Urrea Montoya, Amylkar Patiño Rodríguez, Carmen Elena Usuga Manco, Olga Cecilia |
| dc.contributor.researchgroup.spa.fl_str_mv |
ALIADO - Analítica e Investigación para la Toma de Decisiones |
| dc.subject.lemb.none.fl_str_mv |
Datos estadísticos Statistical data Estimación de parámetros Parameter estimation Análisis de regresión Regression analysis |
| topic |
Datos estadísticos Statistical data Estimación de parámetros Parameter estimation Análisis de regresión Regression analysis |
| description |
ABSTRACT: In some areas of knowledge, we can find phenomena represented by negative variables (R# ) ; having a statistical model is crucial to describe the phenomenon and explain it using other variables. This paper proposes a regression model to analyze negative random variables using the reflected Weibull distribution. This paper reports the RelDists package created in the R programming language to implement the proposed model. A Monte Carlo simulation study was conducted to explore the performance of the estimation procedure. The simulation study encompasses two cases: without covariates and with covariables. In the first case, we only have the response variable to estimate the distribution parameters. In the second case, we have the response variable and two explanatory variables to estimate the model parameters. Additionally, censored and uncensored data were considered in the simulation study. From the simulation study, we found that the estimation procedure achieves accurate estimations of the parameters as the sample size increases and the percentage of censoring decreases. In the paper, we present an application of the proposed model using experimental data from a compression test with concrete specimens. In the application, a model was fitted to explain the shrinkage strain using the variable time. The regression model for negative variables and the RelDists package can be used by academic, scientific, and business communities to perform reliability analysis. |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022 |
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2023-06-02T21:36:25Z |
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2023-06-02T21:36:25Z |
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Artículo de investigación |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Urrea Montoya, A.; Hernández Barajas, F.; Patiño Rodríguez, C.; Usuga Manco, O. (2022). Statistical model for analizing negative variables with application to compression test on concrete. Revista EIA, 19(38), Reia3806. pp. 1-19. https://doi.org/10.24050/reia. v19i38.1526 |
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1794-1237 |
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https://hdl.handle.net/10495/35280 |
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10.24050/reia. v19i38.1526 |
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2463-0950 |
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Urrea Montoya, A.; Hernández Barajas, F.; Patiño Rodríguez, C.; Usuga Manco, O. (2022). Statistical model for analizing negative variables with application to compression test on concrete. Revista EIA, 19(38), Reia3806. pp. 1-19. https://doi.org/10.24050/reia. v19i38.1526 1794-1237 10.24050/reia. v19i38.1526 2463-0950 |
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https://hdl.handle.net/10495/35280 |
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eng |
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eng |
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Revista EIA |
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38 |
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Envigado, Colombia |
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Universidad de Antioquia |
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Hernández Barajas, FreddyUrrea Montoya, AmylkarPatiño Rodríguez, Carmen ElenaUsuga Manco, Olga CeciliaALIADO - Analítica e Investigación para la Toma de Decisiones2023-06-02T21:36:25Z2023-06-02T21:36:25Z2022Urrea Montoya, A.; Hernández Barajas, F.; Patiño Rodríguez, C.; Usuga Manco, O. (2022). Statistical model for analizing negative variables with application to compression test on concrete. Revista EIA, 19(38), Reia3806. pp. 1-19. https://doi.org/10.24050/reia. v19i38.15261794-1237https://hdl.handle.net/10495/3528010.24050/reia. v19i38.15262463-0950ABSTRACT: In some areas of knowledge, we can find phenomena represented by negative variables (R# ) ; having a statistical model is crucial to describe the phenomenon and explain it using other variables. This paper proposes a regression model to analyze negative random variables using the reflected Weibull distribution. This paper reports the RelDists package created in the R programming language to implement the proposed model. A Monte Carlo simulation study was conducted to explore the performance of the estimation procedure. The simulation study encompasses two cases: without covariates and with covariables. In the first case, we only have the response variable to estimate the distribution parameters. In the second case, we have the response variable and two explanatory variables to estimate the model parameters. Additionally, censored and uncensored data were considered in the simulation study. From the simulation study, we found that the estimation procedure achieves accurate estimations of the parameters as the sample size increases and the percentage of censoring decreases. In the paper, we present an application of the proposed model using experimental data from a compression test with concrete specimens. In the application, a model was fitted to explain the shrinkage strain using the variable time. The regression model for negative variables and the RelDists package can be used by academic, scientific, and business communities to perform reliability analysis.RESUMEN: En algunas áreas de conocimiento se pueden presentar fenómenos que son representados por variables aleatorias negativas (R# ) ; contar con un modelo estadístico es crucial para representar esos fenómenos y explicarlos en función de otras variables auxiliares. En este trabajo se propone un modelo de regresión para el análisis de variables aleatorias negativas tomando como distribución para la variable respuesta la distribución Weibull reflejada. En este artículo reportamos el paquete RelDists creado en el lenguaje de programación R para facilitar el uso del modelo de regresión propuesto. Por medio de un estudio de simulación Monte Carlo se exploró el desempeño del proceso de estimación de parámetros. En el estudio de simulación se consideraron dos casos: sin covariables y con covariables. El primer caso se refiere a la situación en la cual sólo se tiene la variable respuesta y con ella se deben estimar los parámetros de la distribución. En el segundo caso se tiene la variable respuesta y variables explicativas que en conjunto se usan para estimar los parámetros del modelo de regresión. Adicionalmente, en el estudio de simulación se consideraron datos censurados y no censurados. Del estudio se encontró que el proceso de estimación logra estimar bien los parámetros del modelo a medida que el tamaño de la muestra aumenta y que el porcentaje de censura disminuye. En el artículo se muestra una aplicación del modelo propuesto usando datos experimentales provenientes de una prueba de contracción con probetas de concreto. En la aplicación se construyó un modelo para explicar la contracción de las probetas en función del tiempo. El modelo de regresión para variables aleatorias negativa y el paquete RelDists pueden ser usados por comunidades académicas, científicas y de negocios para el desarrollo de análisis de confiabilidad.COL003185119application/pdfengEscuela de Ingeniería de AntioquiaEnvigado, Colombiahttp://creativecommons.org/licenses/by-nc-nd/2.5/co/https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Statistical model for analizing negative variables with application to compression test on concreteModelo estadístico para el análisis de variables negativas con aplicación a pruebas de contracción en concretoArtículo de investigaciónhttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionDatos estadísticosStatistical dataEstimación de parámetrosParameter estimationAnálisis de regresiónRegression analysisRevista EIA1938119Revista EIAPublicationLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://bibliotecadigital.udea.edu.co/bitstreams/56a7e0be-7f14-4d62-8aa8-57e31bf5ca60/download8a4605be74aa9ea9d79846c1fba20a33MD53falseAnonymousREADORIGINALUrreaAmylkar_2022_ModeloEstadistico.pdfUrreaAmylkar_2022_ModeloEstadistico.pdfArtículo de investigaciónapplication/pdf2020017https://bibliotecadigital.udea.edu.co/bitstreams/45acb5a8-5ad4-4cd2-b8f4-5e849735729c/downloade7c333135cfc961d8aecffa14c4cad8bMD51trueAnonymousREADCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8823https://bibliotecadigital.udea.edu.co/bitstreams/2d21af34-1a7a-443b-af5e-1798e3066e72/downloadb88b088d9957e670ce3b3fbe2eedbc13MD52falseAnonymousREADTEXTUrreaAmylkar_2022_ModeloEstadistico.pdf.txtUrreaAmylkar_2022_ModeloEstadistico.pdf.txtExtracted texttext/plain47194https://bibliotecadigital.udea.edu.co/bitstreams/a778963b-0a82-4c21-b3e7-a5639fc40ade/downloadca258ea9f887a8f1f5026cef913d284dMD54falseAnonymousREADTHUMBNAILUrreaAmylkar_2022_ModeloEstadistico.pdf.jpgUrreaAmylkar_2022_ModeloEstadistico.pdf.jpgGenerated Thumbnailimage/jpeg12714https://bibliotecadigital.udea.edu.co/bitstreams/8dd6fa7b-c0e3-4c2b-80e1-81a9576b81e9/download0fd6483763f9949b0df7193b46f65b49MD55falseAnonymousREAD10495/35280oai:bibliotecadigital.udea.edu.co:10495/352802025-03-26 23:05:48.076http://creativecommons.org/licenses/by-nc-nd/2.5/co/open.accesshttps://bibliotecadigital.udea.edu.coRepositorio Institucional de la Universidad de Antioquiaaplicacionbibliotecadigitalbiblioteca@udea.edu.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 |