Estimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadístico

dc.contributor.advisorSALAZAR URIBE, JUAN CARLOS
dc.contributor.authorLondoño Ceballos, Catalina
dc.date.accessioned2023-07-17T21:20:45Z
dc.date.available2023-07-17T21:20:45Z
dc.date.issued2023-07
dc.description.abstractEn este trabajo se presenta el ajuste de un modelo estadístico para pronosticar el tamaño del mercado móvil en Colombia (medido en cantidad de líneas) a partir de la utilización de los datos simulados en una de las compañías representativas del sector, con la finalidad de poder tener información oportuna para la toma de decisiones comerciales y tácticas que utilizan dicha información. Como resultado se logró obtener un modelo con márgenes mínimos de error mejorando respecto del modelo lineal normal de referencia, se obtuvo un error medio porcentual absoluto de 0.53 %. (texto tomado de la fuente)spa
dc.description.abstractThis paper presents the adjustment of a statistical model to forecast the size of the mobile market in Colombia (measured in number of lines) from the use of simulated data in one of the representative companies of the sector, with the purpose of being able to have timely information for making business decisions and tactics that use such information. As a result, it was possible to obtain a model with minimum margins of error, improving with respect to the reference normal linear model, an average absolute percentage error of 0.53 % was obtained.eng
dc.description.curricularareaÁrea Curricular Estadísticaspa
dc.description.degreelevelMaestríaspa
dc.description.researchareaAnálisis Multivariado de Datosspa
dc.format.extent95 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.instnameUniversidad Nacional de Colombiaspa
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombiaspa
dc.identifier.repourlhttps://repositorio.unal.edu.co/spa
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/84193
dc.language.isospaspa
dc.publisherUniversidad Nacional de Colombiaspa
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellínspa
dc.publisher.facultyFacultad de Cienciasspa
dc.publisher.placeMedellín, Colombiaspa
dc.publisher.programMedellín - Ciencias - Maestría en Ciencias - Estadísticaspa
dc.relation.indexedLaReferenciaspa
dc.relation.referencesAgresti, A. (2002). Categorical data analysis. John Wiley & Sons.spa
dc.relation.referencesAgresti, A. (2015). Foundations of linear and generalized linear models. John Wiley & Sons.spa
dc.relation.referencesAnnafari, M. T. (2013). Multiple subscriptions of mobile telephony: Explaining the diffusion pattern using sampling data. Telecommunications Policy, 37(10):930–939. Regulating and investment in new communications infrastructure Understanding ICT adoption and market trends: Papers from recent European ITS regional conferencesspa
dc.relation.referencesCameron, A. and Trivedi, P. (1999). Essentials of count data regression. A Companion to Theoretical Econometrics. Malden, MA: Blackwell Publishing Ltd.spa
dc.relation.referencesChu, W.-L., Wu, F.-S., Kao, K.-S., and Yen, D. C. (2009). Diffusion of mobile telephony: An empirical study in Taiwan. Telecommunications Policy, 33(9):506–520.spa
dc.relation.referencesDagum, E. B. and Cholette, P. A. (2006). Benchmarking, temporal distribution, and reconciliation methods for time series.spa
dc.relation.referencesde Myttenaere, A., Golden, B., Le Grand, B., and Rossi, F. (2016). Mean absolute percentage error for regression models. Neurocomputing, 192:38–48. Advances in artificial neural networks, machine learning and computational intelligence.spa
dc.relation.referencesFitzmaurice, G., Laird, N., and Ware, J. (2011). Applied Longitudinal Analysis. Wiley Series in Probability and Statistics. Wileyspa
dc.relation.referencesFitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2012). Applied longitudinal analysis, volume 998. John Wiley & Sons.spa
dc.relation.referencesForthofer, R. N., Lee, E. S., and Hernandez, M. (2007). 3 - descriptive methods. In Forthofer, R. N., Lee, E. S., and Hernandez, M., editors, Biostatistics (Second Edition), pages 21– 69. Academic Press, San Diego, second edition edition.spa
dc.relation.referencesFrees, E. W. et al. (2004). Longitudinal and panel data: analysis and applications in the social sciences. Cambridge University Press.spa
dc.relation.referencesGamboa, L. F. and Otero, J. (2009). An estimation of the pattern of diffusion of mobile phones: The case of Colombia. Telecommunications Policy, 33(10-11):611–620.spa
dc.relation.referencesGamer, M., Lemon, J., and <puspendra.pusp22@gmail.com>, I. F. P. S. (2019). irr: Various Coefficients of Interrater Reliability and Agreement. R package version 0.84.1.spa
dc.relation.referencesHalekoh, U., Højsgaard, S., and Yan, J. (2006). The r package geepack for generalized estimating equations. Journal of Statistical Software, 15/2:1–11.spa
dc.relation.referencesHardin, J. and Hilbe, J. (2013). Generalized estimating equations (second edition).spa
dc.relation.referencesHastie, T. (2022). gam: Generalized Additive Models. R package version 1.20.2.spa
dc.relation.referencesHerrera Giraldo, M. F. (2012). Difusión de la telefonía móvil en Colombia. Master’s thesis.spa
dc.relation.referencesIliinsky, N. and Steele, J. (2011). Designing data visualizations: Representing informational Relationships. O’ Reilly Media, Inc.spa
dc.relation.referencesIslama, M. R. (2014). R program for temporal disaggregation: Denton’s method.spa
dc.relation.referencesJha, A. and Saha, D. (2020). Forecasting and analysing the characteristics of 3G and 4G mobile broadband diffusion in India: A comparative evaluation of Bass, Norton-Bass, Gompertz, and logistic growth models. Technological Forecasting and Social Change, 152:119885.spa
dc.relation.referencesKoo, T. K. and Li, M. Y. (2016). A guideline of selecting and reporting intraclass correlation coefficients for reliability research. Journal of chiropractic medicine, 15(2):155–163.spa
dc.relation.referencesKristoufek, L. (2014). Measuring correlations between non-stationary series with dcca coefficient. Physica A: Statistical Mechanics and its Applications, 402:291–298.spa
dc.relation.referencesLi, G., Lian, H., Feng, S., and Zhu, L. (2013). Automatic variable selection for longitudinal generalized linear models. Computational Statistics & Data Analysis, 61:174–186.spa
dc.relation.referencesLiang, K.-Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1):13–22.spa
dc.relation.referencesLuo, R. and Pan, J. (2022). Conditional generalized estimating equations of mean-variancecorrelation for clustered data. Computational Statistics Data Analysis, 168:107386.spa
dc.relation.referencesMolnar, C. (2020). Interpretable machine learning. Lulu. com.spa
dc.relation.referencesMontgomery, D. C., Peck, E. A., and Vining, G. G. (2012). Introduction to linear regression analysis. John Wiley & Sons.spa
dc.relation.referencesPan, W. (2001). Akaike’s information criterion in generalized estimating equations. Biometrics, 57(1):120–125.spa
dc.relation.referencesPark, T., Davis, C. S., and Li, N. (1998). Alternative gee estimation procedures for discrete longitudinal data. Computational Statistics Data Analysis, 28(3):243–256.spa
dc.relation.referencesPearson, R. K. (2018). Exploratory data analysis using R. Chapman and Hall/CRC.spa
dc.relation.referencesPrass, T. S. and Pumi, G. (2020). DCCA: Detrended Fluctuation and Detrended CrossCorrelation Analysis. R package version 0.1.1.spa
dc.relation.referencesPuth, M.-T., Neuhäuser, M., and Ruxton, G. D. (2015). Effective use of spearman’s and kendall’s correlation coefficients for association between two measured traits. Animal Behaviour, 102:77–84spa
dc.relation.referencesR Core Team (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.spa
dc.relation.referencesRuppert, D., Wand, M. P., and Carroll, R. J. (2003). Semiparametric regression. Cambridge University Press.spa
dc.relation.referencesSax, C. and Steiner, P. (2013). Temporal disaggregation of time series. The R Journal, 5(2):80–87.spa
dc.relation.referencesSeabold, S. and Perktold, J. (2010). statsmodels: Econometric and statistical modeling with python. In 9th Python in Science Conference.spa
dc.relation.referencesShen, C. (2015). Analysis of detrended time-lagged cross-correlation between two nonstationary time series. Physics Letters A, 379(7):680–687.spa
dc.relation.referencesTsai, M.-Y. (2015). Comparison of concordance correlation coefficient via variance components, generalized estimating equations and weighted approaches with model selection. Computational Statistics Data Analysis, 82:47–58.spa
dc.relation.referencesTsai, M.-Y., Wang, J.-F., and Wu, J.-L. (2011). Generalized estimating equations with model selection for comparing dependent categorical agreement data. Computational Statistics Data Analysis, 55(7):2354–2362.spa
dc.relation.referencesUpton, G. and Cook, I. (2014). A dictionary of statistics 3e. Oxford university press.spa
dc.relation.referencesWold, S. (1974). Spline functions in data analysis. Technometrics, 16(1):1–11.spa
dc.relation.referencesWooldridge, J. M. (2015). Introductory econometrics: A modern approach. Cengage learning.spa
dc.relation.referencesWu, F.-S. and Chu, W.-L. (2010). Diffusion models of mobile telephony. Journal of Business Research, 63(5):497–501. TECHNOLOGY MANAGEMENT.spa
dc.relation.referencesZebende, G. (2011). Dcca cross-correlation coefficient: Quantifying level of cross-correlation. Physica A: Statistical Mechanics and its Applications, 390(4):614–618.spa
dc.relation.referencesZiegler, A. (2011). Generalized estimating equations, volume 204. Springer Science & Business Media.spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.licenseAtribución-NoComercial 4.0 Internacionalspa
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/spa
dc.subject.ddc510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasspa
dc.subject.lembModelos lineales (Estadística)
dc.subject.lembProcesos de Poisson
dc.subject.proposalModelos lineales generalizadosspa
dc.subject.proposalGeneralized linear modelseng
dc.subject.proposalGeneralized estimating equationeng
dc.subject.proposalmobile telephonyeng
dc.subject.proposalEstimación de ecuaciones generalizadasspa
dc.subject.proposalModelos estadísticosspa
dc.subject.proposalPronósticospa
dc.subject.proposalTelefonía móvilspa
dc.subject.proposalStatistical modelseng
dc.subject.proposalForecastingeng
dc.subject.wikidataTelefonía móvil
dc.titleEstimación del tamaño del mercado de la telefonía móvil en Colombia a través de un modelo estadísticospa
dc.title.translatedEstimation of the mobile telecommunication market size in Colombia through a statistical modeleng
dc.typeTrabajo de grado - Maestríaspa
dc.type.coarhttp://purl.org/coar/resource_type/c_bdccspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/masterThesisspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TMspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dcterms.audience.professionaldevelopmentReceptores de fondos federales y solicitantesspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa

Archivos

Bloque original

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
1020461755.2023.pdf
Tamaño:
2.5 MB
Formato:
Adobe Portable Document Format
Descripción:
Tesis de Maestría en Ciencias - Estadística

Bloque de licencias

Mostrando 1 - 1 de 1
Cargando...
Miniatura
Nombre:
license.txt
Tamaño:
5.74 KB
Formato:
Item-specific license agreed upon to submission
Descripción: