Nonlinear regression approach to estimating Johnson SB parameters for diameter data
A nonlinear regression approach is proposed to estimate the parameters of the Johnson S(B) distribution. This method was compared to five other methods; these were the four percentile points method, the Knoebel-Burkhart method, the linear regression method, the maximum likelihood (Newton-Raphson) me...
| Main Authors: | , , |
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| Format: | Article |
| Published: |
Canadian Science Publishing
1999
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| Online Access: | http://psasir.upm.edu.my/id/eprint/112716/ |
| _version_ | 1848866016831995904 |
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| author | Abd Kudus, Kamziah Ahmad, M I Lapongan, Jaffirin |
| author_facet | Abd Kudus, Kamziah Ahmad, M I Lapongan, Jaffirin |
| author_sort | Abd Kudus, Kamziah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | A nonlinear regression approach is proposed to estimate the parameters of the Johnson S(B) distribution. This method was compared to five other methods; these were the four percentile points method, the Knoebel-Burkhart method, the linear regression method, the maximum likelihood (Newton-Raphson) method, and the modified maximum likelihood method through simulation. The performance of the nonlinear regression method was also investigated by using the real diameter data collected from 20 even-aged sample plots of the Acacia mangium Willd. plantation in Sandakan, Sabah, measured annually from age 2 to 8 years. Goodness-of-fit tests based on empirical distribution function (namely the Kolmogorov-Smirnov statistic, Cramer- von Mises statistic, and the Anderson-Darling statistic) were used in selecting the most superior parameter estimation method. Results suggested that the nonlinear regression method was superior for estimating parameters of the Johnson S(B) distribution for diameter data in terms of bias, root mean square error, and goodness-of-fit tests. |
| first_indexed | 2025-11-15T14:13:54Z |
| format | Article |
| id | upm-112716 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T14:13:54Z |
| publishDate | 1999 |
| publisher | Canadian Science Publishing |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1127162025-02-05T08:03:41Z http://psasir.upm.edu.my/id/eprint/112716/ Nonlinear regression approach to estimating Johnson SB parameters for diameter data Abd Kudus, Kamziah Ahmad, M I Lapongan, Jaffirin A nonlinear regression approach is proposed to estimate the parameters of the Johnson S(B) distribution. This method was compared to five other methods; these were the four percentile points method, the Knoebel-Burkhart method, the linear regression method, the maximum likelihood (Newton-Raphson) method, and the modified maximum likelihood method through simulation. The performance of the nonlinear regression method was also investigated by using the real diameter data collected from 20 even-aged sample plots of the Acacia mangium Willd. plantation in Sandakan, Sabah, measured annually from age 2 to 8 years. Goodness-of-fit tests based on empirical distribution function (namely the Kolmogorov-Smirnov statistic, Cramer- von Mises statistic, and the Anderson-Darling statistic) were used in selecting the most superior parameter estimation method. Results suggested that the nonlinear regression method was superior for estimating parameters of the Johnson S(B) distribution for diameter data in terms of bias, root mean square error, and goodness-of-fit tests. Canadian Science Publishing 1999 Article PeerReviewed Abd Kudus, Kamziah and Ahmad, M I and Lapongan, Jaffirin (1999) Nonlinear regression approach to estimating Johnson SB parameters for diameter data. Canadian Journal of Forest Research, 29 (3). pp. 310-314. ISSN 0045-5067; eISSN: 1208-6037 https://nrc-prod.literatumonline.com/doi/10.1139/x98-197 10.1139/x98-197 |
| spellingShingle | Abd Kudus, Kamziah Ahmad, M I Lapongan, Jaffirin Nonlinear regression approach to estimating Johnson SB parameters for diameter data |
| title | Nonlinear regression approach to estimating Johnson SB parameters for diameter data |
| title_full | Nonlinear regression approach to estimating Johnson SB parameters for diameter data |
| title_fullStr | Nonlinear regression approach to estimating Johnson SB parameters for diameter data |
| title_full_unstemmed | Nonlinear regression approach to estimating Johnson SB parameters for diameter data |
| title_short | Nonlinear regression approach to estimating Johnson SB parameters for diameter data |
| title_sort | nonlinear regression approach to estimating johnson sb parameters for diameter data |
| url | http://psasir.upm.edu.my/id/eprint/112716/ http://psasir.upm.edu.my/id/eprint/112716/ http://psasir.upm.edu.my/id/eprint/112716/ |