Local Polynomial Smoothing untuk Mengatasi Masalah Age heaping Data Jumlah Kematian Menurut Umur Hasil Sensus Penduduk 2010
DOI:
https://doi.org/10.34123/jurnalasks.v9i2.141Keywords:
age heaping, digit preference, Whippel Index, Myers Index, Local polinomialAbstract
Age data collected through census or survey often experience errors, especially in reporting age data (age missreporting) related to the selection of certain numbers (digit preference) which are usually ages that end with the number 0 (zero) or 5 (five). The existence of this preference digit which results in the age distribution becoming enlarged or accumulated on the numbers ending with 0 and 5, which in demography is known as age-heaping. Evaluation of the quality of age data from the results of the 2010 Population Census in this study focused more on data on the number of deaths by age is very necessary. This data will be evaluated using the Whipple Index (IW) and Myers (IM) Index. The technique of smoothing local polynomials in non-parametric regression with IW and IM resulted in the conclusion that there was age heaping in the data that became the object of this study so that data needs to be smoothed with local polynomial techniques.
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References
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Thomas, J. (2012). Univariate graduation mortality by local polynomial regression. Bulletin Francais D’Actuariat, 12, 5-58.
Tibshirani, R. dan Hastie, T. (1987). Local likelihood estimation. Journal of the American Statistical Association, 82, 559-567.
Wasserman, L. (2005). All of Nonparametric Statistics. New York: Springer.
Camarda, Ellers dan Gampe (2008). Modelling general patterns of digit preference. Statistical Modelling 2008; 8(4): 385--401
Chaudhuri, P. dan Dewanji, A. (1995). On a likelihood-based approach in nonparametric smoothing and cross-validation. Statistics and Probability Letters, 22, 7-15.
Consul, P.C. dan Jain, G.C. (1973). A generalization of the Poisson distribution. Technometrics,15(4),791-799.
Currie, I..D., Durban, M. dan Eilers, P.H.C. (2004). Smoothing and forecasting mortality rates. Statistical Modelling, 4, 279-298.
Famoye, F. (1993). Restricted Generalized Poisson regression. Communication in Statistics-Theory and Methods, 33, 1135-1154.
Fan, J. dan Gijbels, I. (1996). Local Polynomial Modelling and Its Application . London: Chapman and Hall.
Fumihiko (2013). ???????? ? ?? ???????????????Age heaping, Paper, JICA.
Hardle, W. (1990). Applied Nonparametric Regression. Boston: Cambridge University Press.
Hastie, T.J. dan Tibshirani, R.J. (1990). Generalized Additive Models. London: Chapman & Hall.
Heligman, M. dan Pollard, J.H. (1980). The age pattern of mortality. Journal of the Institute of Actuaries, 107, 49-80.
Hill, M.E., S.H. Preston, and I. Rosenwaike. (2000). ‘Age Reporting among White Americans Aged 85+: Results of a Record Linkage Study’. Demography, 37:175-186.
Hyndman, R.J., dan Ullah, M.S. (2007). Robust forecasting of mortallity and fertility rates: a functional data approach. Computational Statistics and Data Analysis, 51, 4942-4956.
Junaidi (2010). Indeks Whipple : Evaluasi Pelaporan Umur Penduduk. 20 Juni 2014. https://junaidichaniago.wordpress.com/2010/05/26/indeks-whipple-evaluasi-pelaporan-umur-penduduk/
Kidane, A. ( 2102). Digit Preference in African Survey Data and Their Impact on Parametric estimates, presented at the African Econometric Society Conference July 11-13 2009 Abuja, Nigeria
Lee, R.D. dan Carter, L.R. (1992). Modeling and forecasting U.S. mortality. Journal of the American Statistical Association, 87, 659-675.
Kabir M and Chowdhury K. (1981). The pattern of age reporting errors in the districts of Bangladesh. Rural Demography Journal, vol 8, no. 2
Mason, K.O and Cope, L.G. (1987, November). Sources of age and date of birth misreporting in 1900 U.S. census. Journal of Demography, vol 24, no. 4
Mc Cullagh, P. dan Nelder, J.A. (1989). Generalized Linear Models. London: Chapman and Hall.
Myers RJ (1940) Errors and bias in the reporting of ages in census data. Transactions of the Actuarial Society of America, 41(Pt. 2 (104)), 395–415.
Mukherjee, B.N and Mukhopadhyay, B.K. (1988, Jan.-June). A study of digit preference and quality of age data in Turkish censuses. Genus, vol. 44, no. 1-2
Nagi M.H., Stockwell, E.G and Snavley, L.M. (1973, August). Digit preference and avoidance in the age statistics of some recent African censuses: some patterns and correlates. International Statistical Review, vol. 41, no. 2.
Nasir dan Hinde (2014). An Extension Of Modified Whipple Index– Further Modified Whipple Index. Pak. J. Statist. 2014 Vol. 30(2), 265-272
Noumbissi, A. (1992). L'indice de Whipple modifié: une application aux données du Cameroun, de la Suède et de la Belgique. Population (French Ed.) 47(4), 1038-1041.
Peristera, P. dan Kostaky, A. (2005). An evaluation of the performance of kernel estimator for graduating mortality data. Journal of Population Research, 22, 185-197.
Santos, J.A. dan Neves, M.M. (2008). A local maximum likelihood estimator for poisson regression. Metrika, 68, 257-270.
Shyamalkumar, N.D. (2006). Anaysis of mortality data using smoothing spline poisson regression. Working Paper. Dept. of Stat.and Actuarial Science, The University of Iowa.
Sonderegger, D.L. (2010). Nonparametric function smoothing: fiducial inference of free knot splines and ecological applications. Dissertation, Colorado State University, Colorado.
Spoorenberg, T. (2007). Quality of Age Reporting: Extension and Application of the Modified Whipple's Index. Population (English Edition, 2002), 62(4), 729-741.
Thomas, J. (2012). Univariate graduation mortality by local polynomial regression. Bulletin Francais D’Actuariat, 12, 5-58.
Tibshirani, R. dan Hastie, T. (1987). Local likelihood estimation. Journal of the American Statistical Association, 82, 559-567.
Wasserman, L. (2005). All of Nonparametric Statistics. New York: Springer.
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Published
2017-12-31
How to Cite
Firdaus, F., & Astuti, E. T. (2017). Local Polynomial Smoothing untuk Mengatasi Masalah Age heaping Data Jumlah Kematian Menurut Umur Hasil Sensus Penduduk 2010. Jurnal Aplikasi Statistika & Komputasi Statistik, 9(2), 1–18. https://doi.org/10.34123/jurnalasks.v9i2.141
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