Fauziah Maarof

Statistical models related to accumulated biomass of Hopea odorata in three soil series of ultisols

This paper presents results on statistical distribution fitting and polynomial regression on accumulated biomass of seven year-old tree species Hopea odorata, which were planted in three different soil series of ultisols, namely Rengggam, Baling and Kuala Berang. Data were collected from a study conducted in a Hopea odorata plantation at FRIM Research Station, Segamat, Johor. Thirty tree stands in each soil series were randomly sampled to measure their growth performance and accumulated biomass. The Kolmogorov-Smirnov and Anderson-Darling tests indicated that for all tree stands, diameter, height, above ground (stem, branch, leaves) and below ground (root) biomass were found to be best fitted with the four parameters Johnsons System Bounded (SB) distributions. Then, for each soil series, a polynomial regression model was estimated to describe the relationship between total accumulated biomass and functions of tree diameter and height.

Advantage of the modified Lunn-Mcneil technique over Kalbfleisch-Prentice technique in competing risks

Survival analysis algorithm is often applied in the data mining process. Cox regression is one of the survival analysis tools that has been used in many areas, and it can be used to analyze the failure times of aircraft crashed. Another survival analysis tool is the competing risks where we have more than one cause of failure acting simultaneously. Lunn-McNeil analyzed the competing risks in the survival model using Cox regression with censored data. The modified Lunn-McNeil technique is a simplify of the Lunn-McNeil technique. The Kalbfleisch-Prentice technique is involving fitting models separately from each type of failure, treating other failure types as censored. To compare the two techniques, (the modified Lunn-McNeil and Kalbfleisch-Prentice) a simulation study was performed. Samples with various sizes and censoring percentages were generated and fitted using both techniques. The study was conducted by comparing the inference of models, using Root Mean Square Error (RMSE), the power tests, and the Schoenfeld residual analysis. The power tests in this study were likelihood ratio test, Rao-score test, and Wald statistics. The Schoenfeld residual analysis was conducted to check the proportionality of the model through its covariates. The estimated parameters were computed for the cause-specific hazard situation. Results showed that the modified Lunn-McNeil technique was better than the Kalbfleisch-Prentice technique based on the RMSE measurement and Schoenfeld residual analysis. However, the Kalbfleisch-Prentice technique was better than the modified Lunn-McNeil technique based on power tests measurement.