Matches in SemOpenAlex for { <https://semopenalex.org/work/W3205718487> ?p ?o ?g. }
Showing items 1 to 87 of
87
with 100 items per page.
- W3205718487 endingPage "11" @default.
- W3205718487 startingPage "1" @default.
- W3205718487 abstract "Cancer is among the major public health problems as well as a burden for Pakistan. About 148,000 new patients are diagnosed with cancer each year, and almost 100,000 patients die due to this fatal disease. Lung, breast, liver, cervical, blood/bone marrow, and oral cancers are the most common cancers in Pakistan. Perhaps smoking, physical inactivity, infections, exposure to toxins, and unhealthy diet are the main factors responsible for the spread of cancer. We preferred a novel four-component mixture model under Bayesian estimation to estimate the average number of incidences and death of both genders in different age groups. For this purpose, we considered 28 different kinds of cancers diagnosed in recent years. Data of registered patients all over Pakistan in the year 2012 were taken from GLOBOCAN. All the patients were divided into 4 age groups and also split based on genders to be applied to the proposed mixture model. Bayesian analysis is performed on the data using a four-component exponential mixture model. Estimators for mixture model parameters are derived under Bayesian procedures using three different priors and two loss functions. Simulation study and graphical representation for the estimates are also presented. It is noted from analysis of real data that the Bayes estimates under LINEX loss assuming Jeffreys' prior is more efficient for the no. of incidences in male and female. As far as no. of deaths are concerned again, LINEX loss assuming Jeffreys' prior gives better results for the male population, but for the female population, the best loss function is SELF assuming Jeffreys' prior." @default.
- W3205718487 created "2021-10-25" @default.
- W3205718487 creator A5009396276 @default.
- W3205718487 creator A5017457371 @default.
- W3205718487 creator A5020286648 @default.
- W3205718487 creator A5041738870 @default.
- W3205718487 creator A5056886115 @default.
- W3205718487 creator A5078719484 @default.
- W3205718487 creator A5085199509 @default.
- W3205718487 date "2021-10-12" @default.
- W3205718487 modified "2023-09-27" @default.
- W3205718487 title "Bayesian Analysis of Cancer Data Using a 4-Component Exponential Mixture Model" @default.
- W3205718487 cites W1970592510 @default.
- W3205718487 cites W2014766989 @default.
- W3205718487 cites W2050009269 @default.
- W3205718487 cites W2083129711 @default.
- W3205718487 cites W2164665019 @default.
- W3205718487 cites W2488678869 @default.
- W3205718487 cites W2963281869 @default.
- W3205718487 cites W3022392951 @default.
- W3205718487 cites W4300501217 @default.
- W3205718487 cites W4300956553 @default.
- W3205718487 doi "https://doi.org/10.1155/2021/6289337" @default.
- W3205718487 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/8526261" @default.
- W3205718487 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34675992" @default.
- W3205718487 hasPublicationYear "2021" @default.
- W3205718487 type Work @default.
- W3205718487 sameAs 3205718487 @default.
- W3205718487 citedByCount "2" @default.
- W3205718487 countsByYear W32057184872022 @default.
- W3205718487 countsByYear W32057184872023 @default.
- W3205718487 crossrefType "journal-article" @default.
- W3205718487 hasAuthorship W3205718487A5009396276 @default.
- W3205718487 hasAuthorship W3205718487A5017457371 @default.
- W3205718487 hasAuthorship W3205718487A5020286648 @default.
- W3205718487 hasAuthorship W3205718487A5041738870 @default.
- W3205718487 hasAuthorship W3205718487A5056886115 @default.
- W3205718487 hasAuthorship W3205718487A5078719484 @default.
- W3205718487 hasAuthorship W3205718487A5085199509 @default.
- W3205718487 hasBestOaLocation W32057184871 @default.
- W3205718487 hasConcept C105795698 @default.
- W3205718487 hasConcept C107673813 @default.
- W3205718487 hasConcept C121608353 @default.
- W3205718487 hasConcept C126322002 @default.
- W3205718487 hasConcept C177769412 @default.
- W3205718487 hasConcept C207201462 @default.
- W3205718487 hasConcept C2908647359 @default.
- W3205718487 hasConcept C33923547 @default.
- W3205718487 hasConcept C530470458 @default.
- W3205718487 hasConcept C61224824 @default.
- W3205718487 hasConcept C71924100 @default.
- W3205718487 hasConcept C99454951 @default.
- W3205718487 hasConceptScore W3205718487C105795698 @default.
- W3205718487 hasConceptScore W3205718487C107673813 @default.
- W3205718487 hasConceptScore W3205718487C121608353 @default.
- W3205718487 hasConceptScore W3205718487C126322002 @default.
- W3205718487 hasConceptScore W3205718487C177769412 @default.
- W3205718487 hasConceptScore W3205718487C207201462 @default.
- W3205718487 hasConceptScore W3205718487C2908647359 @default.
- W3205718487 hasConceptScore W3205718487C33923547 @default.
- W3205718487 hasConceptScore W3205718487C530470458 @default.
- W3205718487 hasConceptScore W3205718487C61224824 @default.
- W3205718487 hasConceptScore W3205718487C71924100 @default.
- W3205718487 hasConceptScore W3205718487C99454951 @default.
- W3205718487 hasLocation W32057184871 @default.
- W3205718487 hasLocation W32057184872 @default.
- W3205718487 hasLocation W32057184873 @default.
- W3205718487 hasLocation W32057184874 @default.
- W3205718487 hasOpenAccess W3205718487 @default.
- W3205718487 hasPrimaryLocation W32057184871 @default.
- W3205718487 hasRelatedWork W1541815981 @default.
- W3205718487 hasRelatedWork W1543123847 @default.
- W3205718487 hasRelatedWork W1987768441 @default.
- W3205718487 hasRelatedWork W1995246512 @default.
- W3205718487 hasRelatedWork W2015748835 @default.
- W3205718487 hasRelatedWork W2133205540 @default.
- W3205718487 hasRelatedWork W3160546271 @default.
- W3205718487 hasRelatedWork W4245459252 @default.
- W3205718487 hasRelatedWork W4300552941 @default.
- W3205718487 hasRelatedWork W4300987207 @default.
- W3205718487 hasVolume "2021" @default.
- W3205718487 isParatext "false" @default.
- W3205718487 isRetracted "false" @default.
- W3205718487 magId "3205718487" @default.
- W3205718487 workType "article" @default.