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Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution

Yıl 2024, , 84 - 100, 31.08.2024
https://doi.org/10.54187/jnrs.1504722

Öz

Bladder cancer is among the ten most common types of cancer worldwide, with approximately 550,000 new cases occurring each year. It accounts for comprehensively compared to 3% of all newly diagnosed cancer cases and contributes to 2.1% of cancer-related deaths globally. This article introduces goodness-of-fit tests that aim to fit the exponentialized exponential distribution. These tests are based on the Kullback-Leibler difference and have been applied to censored and complete samples of Bladder Cancer Patients. We calculated critical values and statistical power measurements, considering the best and worst bandwidth scenarios. We then comprehensively compared essential values and power across various parameters, accounting for optimal and suboptimal bandwidth choices derived from the Kullback–Leibler difference. In the final phase of our study, we used a dataset of individuals diagnosed with bladder cancer to demonstrate the practical applicability of our proposed research. Finally, this modeling type can benefit researchers and healthcare professionals through time-to-event analysis (survival analysis), investigation of events, medical decision-making, and risk prediction.

Etik Beyan

No approval from the Board of Ethics is required.

Kaynakça

  • F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68 (6) (2018) 394-424.
  • R. B. D'agostino, M. A. Stephens, Goodness of fit techniques. New York, MDI Press, 1986.
  • K. Pearson, On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 50 (302) (1900) 157-175.
  • I. Arizono, H. Ohta, A Test for normality based on Kullback-Leibler information, The American Statistician 43 (1989) 20-22.
  • B. Şenoğlu, B. Sürücü, Goodness-of-fit tests based on Kullback-Leibler information, IEEE Transactions on Reliability 53 (2004) 357-361.
  • B. Choi, K. Kim, S. Heun Song, Goodness-of-fit test for exponentiality based on Kullback–Leibler information, Communications in Statistics - Simulation and Computation 33 (2004) 525-536.
  • S. Park, Testing exponentiality based on the Kullback-Leibler information with the type II censored data, IEEE Transactions on Reliability 54 (2005) 22-26.
  • J. Lim, S. Park, A goodness of fit tests based on the partial Kullback-Leibler information with the type II censored data, Journal of Applied Statistics 34 (2007) 1051-1064.
  • N. Balakrishnan, A. H. Rad, N. R. Arghami, Testing exponentiality based on Kullback-Leibler information with progressively type-II censored data, IEEE Transactions on Reliability 56 (2007) 301-307.
  • J. Lim, S. Park, Censored Kullback-Leibler information and goodness-of-fit test with type II censored data, Quality control and applied statistics 54 (1) (2009) 79-80.
  • A. H. Rad, F. Yousefzadeh, N. Balakrishnan, Goodness-of-fit test based on Kullback-Leibler information for progressively type-II censored data, IEEE Transactions on Reliability 60 (2011) 570-579.
  • G. Gurevich, A. Davidson, Standardized forms of Kullback-Leibler information-based statistics for normality and exponentiality. Computer Modelling and New Technologies 12(1) (2008) 14-25.
  • S. Park, R. Pakyari, Cumulative residual Kullback–Leibler information with the progressively Type-II censored data, Statistics & Probability Letters 106 (2015) 287-294.
  • E. Elsherpieny, H. Muhammed, N. Radwan, On Discriminating between gamma and log-logistic distributions in case of progressive type-II censoring, Pakistan Journal of Statistics and Operation Research 13 (2017) 157.
  • M. Bitaraf, M. Rezaei, F. Yousefzadeh, Goodness-of-fit tests based on Verma Kullback–Leibler information, Communications in Statistics - Theory and Methods 46 (24) (2017) 12152-12164.
  • H. A. Noughabi, Testing exponentiality based on Kullback—Leibler information for progressively type II censored data, Communications in Statistics - Simulation and Computation 46 (2017) 7624-7638.
  • R. D. Gupta D. Kundu, Theory & methods: Generalized exponential distributions, The Australian & New Zealand Journal of Statistics 41 (1999) 173-188.
  • J. C. Correa, A new estimator of entropy, Communications in Statistics - Theory and Methods 24 (1995) 2439-2449.
  • B. Van Es, Estimating functionals related to a density by a class of statistics based on spacings, Scandinavian Journal of Statistics 19 (1992) 61-72.
  • O. Vasicek, A test for normality based on sample entropy, Journal of the Royal Statistical Society. Series B (Methodological) 38 (1976) 54-59.
  • H. A. Noughabi, Testing exponentiality based on Kullback-Leibler information for progressively Type II censored data, Communications in Statistics-Simulation and Computation 46 (10) (2017) 7624-7638.
  • K. Abbas, Z. Hussain, N. Rashid, A. Ali, M. Taj, S. A. Khan, S. Manzoor, U. Khalil, D. M. Khan, Bayesian estimation of Gumbel type-II distribution under type-II censoring with medical applications, Hindawi Computational and Mathematical Methods in Medicine Article ID 1876073 (2020) 11 pages.
Yıl 2024, , 84 - 100, 31.08.2024
https://doi.org/10.54187/jnrs.1504722

Öz

Kaynakça

  • F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, A. Jemal, Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68 (6) (2018) 394-424.
  • R. B. D'agostino, M. A. Stephens, Goodness of fit techniques. New York, MDI Press, 1986.
  • K. Pearson, On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling, The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 50 (302) (1900) 157-175.
  • I. Arizono, H. Ohta, A Test for normality based on Kullback-Leibler information, The American Statistician 43 (1989) 20-22.
  • B. Şenoğlu, B. Sürücü, Goodness-of-fit tests based on Kullback-Leibler information, IEEE Transactions on Reliability 53 (2004) 357-361.
  • B. Choi, K. Kim, S. Heun Song, Goodness-of-fit test for exponentiality based on Kullback–Leibler information, Communications in Statistics - Simulation and Computation 33 (2004) 525-536.
  • S. Park, Testing exponentiality based on the Kullback-Leibler information with the type II censored data, IEEE Transactions on Reliability 54 (2005) 22-26.
  • J. Lim, S. Park, A goodness of fit tests based on the partial Kullback-Leibler information with the type II censored data, Journal of Applied Statistics 34 (2007) 1051-1064.
  • N. Balakrishnan, A. H. Rad, N. R. Arghami, Testing exponentiality based on Kullback-Leibler information with progressively type-II censored data, IEEE Transactions on Reliability 56 (2007) 301-307.
  • J. Lim, S. Park, Censored Kullback-Leibler information and goodness-of-fit test with type II censored data, Quality control and applied statistics 54 (1) (2009) 79-80.
  • A. H. Rad, F. Yousefzadeh, N. Balakrishnan, Goodness-of-fit test based on Kullback-Leibler information for progressively type-II censored data, IEEE Transactions on Reliability 60 (2011) 570-579.
  • G. Gurevich, A. Davidson, Standardized forms of Kullback-Leibler information-based statistics for normality and exponentiality. Computer Modelling and New Technologies 12(1) (2008) 14-25.
  • S. Park, R. Pakyari, Cumulative residual Kullback–Leibler information with the progressively Type-II censored data, Statistics & Probability Letters 106 (2015) 287-294.
  • E. Elsherpieny, H. Muhammed, N. Radwan, On Discriminating between gamma and log-logistic distributions in case of progressive type-II censoring, Pakistan Journal of Statistics and Operation Research 13 (2017) 157.
  • M. Bitaraf, M. Rezaei, F. Yousefzadeh, Goodness-of-fit tests based on Verma Kullback–Leibler information, Communications in Statistics - Theory and Methods 46 (24) (2017) 12152-12164.
  • H. A. Noughabi, Testing exponentiality based on Kullback—Leibler information for progressively type II censored data, Communications in Statistics - Simulation and Computation 46 (2017) 7624-7638.
  • R. D. Gupta D. Kundu, Theory & methods: Generalized exponential distributions, The Australian & New Zealand Journal of Statistics 41 (1999) 173-188.
  • J. C. Correa, A new estimator of entropy, Communications in Statistics - Theory and Methods 24 (1995) 2439-2449.
  • B. Van Es, Estimating functionals related to a density by a class of statistics based on spacings, Scandinavian Journal of Statistics 19 (1992) 61-72.
  • O. Vasicek, A test for normality based on sample entropy, Journal of the Royal Statistical Society. Series B (Methodological) 38 (1976) 54-59.
  • H. A. Noughabi, Testing exponentiality based on Kullback-Leibler information for progressively Type II censored data, Communications in Statistics-Simulation and Computation 46 (10) (2017) 7624-7638.
  • K. Abbas, Z. Hussain, N. Rashid, A. Ali, M. Taj, S. A. Khan, S. Manzoor, U. Khalil, D. M. Khan, Bayesian estimation of Gumbel type-II distribution under type-II censoring with medical applications, Hindawi Computational and Mathematical Methods in Medicine Article ID 1876073 (2020) 11 pages.
Toplam 22 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular İstatistiksel Teori, Uygulamalı İstatistik
Bölüm Articles
Yazarlar

Gülcan Gencer 0000-0002-3543-041X

Erken Görünüm Tarihi 30 Ağustos 2024
Yayımlanma Tarihi 31 Ağustos 2024
Gönderilme Tarihi 25 Haziran 2024
Kabul Tarihi 27 Ağustos 2024
Yayımlandığı Sayı Yıl 2024

Kaynak Göster

APA Gencer, G. (2024). Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution. Journal of New Results in Science, 13(2), 84-100. https://doi.org/10.54187/jnrs.1504722
AMA Gencer G. Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution. JNRS. Ağustos 2024;13(2):84-100. doi:10.54187/jnrs.1504722
Chicago Gencer, Gülcan. “Goodness-of-Fit Tests Based on Kullback-Leibler Divergence for Bladder Cancer Survival Analysis: Applications to Exponentiated Exponential Distribution”. Journal of New Results in Science 13, sy. 2 (Ağustos 2024): 84-100. https://doi.org/10.54187/jnrs.1504722.
EndNote Gencer G (01 Ağustos 2024) Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution. Journal of New Results in Science 13 2 84–100.
IEEE G. Gencer, “Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution”, JNRS, c. 13, sy. 2, ss. 84–100, 2024, doi: 10.54187/jnrs.1504722.
ISNAD Gencer, Gülcan. “Goodness-of-Fit Tests Based on Kullback-Leibler Divergence for Bladder Cancer Survival Analysis: Applications to Exponentiated Exponential Distribution”. Journal of New Results in Science 13/2 (Ağustos 2024), 84-100. https://doi.org/10.54187/jnrs.1504722.
JAMA Gencer G. Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution. JNRS. 2024;13:84–100.
MLA Gencer, Gülcan. “Goodness-of-Fit Tests Based on Kullback-Leibler Divergence for Bladder Cancer Survival Analysis: Applications to Exponentiated Exponential Distribution”. Journal of New Results in Science, c. 13, sy. 2, 2024, ss. 84-100, doi:10.54187/jnrs.1504722.
Vancouver Gencer G. Goodness-of-fit tests based on Kullback-Leibler divergence for bladder cancer survival analysis: Applications to exponentiated exponential distribution. JNRS. 2024;13(2):84-100.


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