M. R. Norazan

Universiti Teknologi MARA

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W. H. Wan Zakiyatussariroh

Universiti Teknologi MARA

A. H. M. Rahmatullah Imon

Ball State University

Z. Mohammad Said

Universiti Teknologi MARA

Habshah Midi

Universiti Putra Malaysia

Mohammad Said Zainol

Universiti Teknologi MARA

Habshah Midi

Universiti Putra Malaysia

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Universiti Teknologi MARA

Universiti Putra Malaysia

Universiti Malaysia Kelantan

Ball State University

Varendra University

Primeasia University

University of Rajshahi

Dijlah University College

Taraba State University

Abubakar Tafawa Balewa University

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Evaluating the performance of the Lee-Carter method and its variants in modelling and forecasting Malaysian mortality

AIP conference proceedings (2014)

W. H. Wan Zakiyatussariroh Z. Mohammad Said M. R. Norazan

This study investigated the performance of the Lee-Carter (LC) method and it variants in modeling and forecasting Malaysia mortality. These include the original LC, the Lee-Miller (LM) variant and the Booth-Maindonald-Smith (BMS) variant. These methods were evaluated using Malaysia's mortality data which was measured based on age specific death rates (ASDR) for 1971 to 2009 for overall population while those for 1980–2009 were used in separate models for male and female population. The performance of the variants has been examined in term of the goodness of fit of the models and forecasting accuracy. Comparison was made based on several criteria namely, mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE). The results indicate that BMS method was outperformed in in-sample fitting for overall population and when the models were fitted separately for male and female population. However, in the case of out-sample forecast accuracy, BMS method only best when the data were fitted to overall population. When the data were fitted separately for male and female, LCnone performed better for male population and LM method is good for female population.

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10.1063/1.4903668

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Citations (2)

THE PERFORMANCE OF LEE–CARTER STATE SPACE MODEL WITH EQUAL AND UNEQUAL VARIANCE ASSUMPTIONS IN FORECASTING MORTALITY RATES

W. H. Wan Zakiyatussariroh Mohammad Said Zainol M. R. Norazan

State-space representation

10.1142/9789813142725_0004

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Estimating regression coefficients using weighted bootstrap with probability

M. R. Norazan Habshah Midi A. H. M. Rahmatullah Imon

In this paper we propose a new Weighted Bootstrap with Probability (WBP). The basic idea of the proposed bootstrap technique is to do re-sampling with probabilities. These probabilities become the control mechanism for getting good estimates when the original data set contain multiple outliers. Numerical examples and simulation study are carried out to evaluate the performance of the WBP estimates as compared to the Bootstrap 1 and Diagnostic-Before Bootstrap estimates. The results of the study signify that the WBP method is more efficient than the other two methods.

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The performance of diagnostic-robust generalized potentials for the identification of multiple high leverage points in linear regression

Journal of Applied Statistics (2009)

Habshah Midi M. R. Norazan A. H. M. Rahmatullah Imon

Abstract Leverage values are being used in regression diagnostics as measures of influential observations in the $X$-space. Detection of high leverage values is crucial because of their responsibility for misleading conclusion about the fitting of a regression model, causing multicollinearity problems, masking and/or swamping of outliers, etc. Much work has been done on the identification of single high leverage points and it is generally believed that the problem of detection of a single high leverage point has been largely resolved. But there is no general agreement among the statisticians about the detection of multiple high leverage points. When a group of high leverage points is present in a data set, mainly because of the masking and/or swamping effects the commonly used diagnostic methods fail to identify them correctly. On the other hand, the robust alternative methods can identify the high leverage points correctly but they have a tendency to identify too many low leverage points to be points of high leverages which is not also desired. An attempt has been made to make a compromise between these two approaches. We propose an adaptive method where the suspected high leverage points are identified by robust methods and then the low leverage points (if any) are put back into the estimation data set after diagnostic checking. The usefulness of our newly proposed method for the detection of multiple high leverage points is studied by some well-known data sets and Monte Carlo simulations. Keywords: diagnostic-robust generalized potentialsgroup deletionhigh leverage pointsmaskingrobust Mahalanobis distanceminimum volume ellipsoidMonte Carlo simulation Acknowledgements The authors gratefully acknowledge various helpful comments and suggestions made by the reviewers.

Leverage (statistics)

Mahalanobis distance

Robust regression

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10.1080/02664760802553463

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Citations (66)

Lee-Carter state space modeling: Application to the Malaysia mortality data

AIP conference proceedings (2014)

W. H. Wan Zakiyatussariroh Z. Mohammad Said M. R. Norazan

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation W. H. Wan Zakiyatussariroh, Z. Mohammad Said, M. R. Norazan; Lee-Carter state space modeling: Application to the Malaysia mortality data. AIP Conference Proceedings 19 June 2014; 1602 (1): 1002–1008. https://doi.org/10.1063/1.4882606 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search

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10.1063/1.4882606

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Citations (1)