Biomass Modelling ofAndrostachys johnsoniiPrain: A Comparison of Three Methods to Enforce Additivity

International Journal of Forestry Research ◽

10.1155/2015/878402 ◽

2015 ◽

Vol 2015 ◽

pp. 1-17 ◽

Cited By ~ 3

Author(s):

Tarquinio Mateus Magalhães ◽

Thomas Seifert

Keyword(s):

Seemingly Unrelated Regression ◽

Tree Biomass ◽

Tree Model ◽

Fit Statistics ◽

Significant Bias ◽

Parameter Restriction ◽

Unrelated Regression ◽

Tree Component ◽

Component Models ◽

Total Tree

Three methods of enforcing additivity of tree component biomass estimates into total tree biomass estimates forAndrostachys johnsoniiPrain were studied and compared, namely, the conventional (CON) method (a method that consists of using the same independent variables for all tree component models, and for total tree model, and the same weights to enforce additivity), seemingly unrelated regression (SUR) with parameter restriction, and nonlinear seemingly unrelated regression (NSUR) with parameter restriction. The CON method was found to be statistically superior to any other method of enforcing additivity, yielding excellent fit statistics and unbiased biomass estimates. The NSUR method ranked second best but was found to be biased. The SUR method was found to be the worst; it exhibited large bias and had a poor fit for the biomass. Therefore, we recommend that only the CON and NSUR methods should be used for further estimates, provided that their limitations are considered, that is, exclusion of contemporaneous correlations for the CON method and consideration of the significant bias of the NSUR method.

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Additive biomass equations for slash pine trees: comparing three modeling approaches

Canadian Journal of Forest Research ◽

10.1139/cjfr-2018-0246 ◽

2019 ◽

Vol 49 (1) ◽

pp. 27-40 ◽

Cited By ~ 10

Author(s):

Dehai Zhao ◽

James Westfall ◽

John W. Coulston ◽

Thomas B. Lynch ◽

Bronson P. Bullock ◽

...

Keyword(s):

Pinus Elliottii ◽

Slash Pine ◽

Seemingly Unrelated Regression ◽

Total Biomass ◽

Tree Biomass ◽

Biomass Equation ◽

Fit Statistics ◽

Biomass Equations ◽

Pine Trees ◽

Total Tree

Both aggregative and disaggregative strategies were used to develop additive nonlinear biomass equations for slash pine (Pinus elliottii Engelm. var. elliottii) trees in the southeastern United States. In the aggregative approach, the total tree biomass equation was specified by aggregating the expectations of component biomass models, and their parameters were estimated by jointly fitting all component and total biomass equations using weighted nonlinear seemingly unrelated regression (NSUR) (SUR1) or by jointly fitting component biomass equations using weighted NSUR (SUR2). In an alternative disaggregative approach (DRM), the biomass component proportions were modeled using Dirichlet regression, and the estimated total tree biomass was disaggregated into biomass components based on their estimated proportions. There was no single system to predict biomass that was best for all components and total tree biomass. The ranking of the three systems based on an array of fit statistics followed the order of SUR2 > SUR1 > DRM. All three systems provided more accurate biomass predictions than previously published equations.

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Tree Biomass Equations for Naturally Regenerated Shortleaf Pine

Southern Journal of Applied Forestry ◽

10.1093/sjaf/32.4.163 ◽

2008 ◽

Vol 32 (4) ◽

pp. 163-167 ◽

Cited By ~ 1

Author(s):

Charles O. Sabatia ◽

Thomas B. Lynch ◽

Rodney E. Will

Keyword(s):

Stand Density ◽

Seemingly Unrelated Regression ◽

Tree Level ◽

Parameter Estimates ◽

Pinus Echinata ◽

Shortleaf Pine ◽

Tree Biomass ◽

Biomass Component ◽

Tree Component ◽

Old Trees

Abstract Aboveground tree-level and branch-level biomass component equations were fitted by nonlinear seemingly unrelated regression, for even-aged naturally regenerated shortleaf pine (Pinus echinata Mill.) in southeastern Oklahoma. Data were obtained from 46- to 53-year-old trees growing in stands that had previously been thinned to densities ranging from 50% of full stocking to overstocked unthinned stands. Stand density affected some of the parameter estimates for trees growing in thinned stands versus unthinned stands. Equations based on dbh alone gave biomass estimates that were not significantly different from those obtained with equations based on dbh, height, and/or crown width. The fitted tree-level biomass component equations were additive in the sense that predictions for biomass components were constrained by the estimation process to sum to total tree biomass. These equations can be used to estimate aboveground tree or tree component biomass for naturally regenerated shortleaf pine in the dbh range of 7–40 cm in southeastern Oklahoma and have potential for application in other shortleaf pine growing areas.

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A Bayesian Approach to Estimating Seemingly Unrelated Regression for Tree Biomass Model Systems

Forests ◽

10.3390/f11121302 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1302

Author(s):

Longfei Xie ◽

Fengri Li ◽

Lianjun Zhang ◽

Faris Rafi Almay Widagdo ◽

Lihu Dong

Keyword(s):

Bayesian Approach ◽

Bayesian Methods ◽

Model Fitting ◽

Seemingly Unrelated Regression ◽

Model Systems ◽

Informative Priors ◽

Tree Biomass ◽

Tree Diameter ◽

Biomass Models ◽

Unrelated Regression

Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SUR) was used to fit the simultaneous equations of four tree components (i.e., stem, branch, foliage, and root). The model parameters were estimated by feasible generalized least squares (FGLS) and Bayesian methods using either non-informative priors or informative priors. The results showed that adding tree height to the model systems improved the model fitting and performance for the stem, branch, and foliage biomass models, but much less for the root biomass models. The Bayesian methods on the SUR models produced narrower 95% prediction intervals than did the classical FGLS method, indicating higher computing efficiency and more stable model predictions, especially for small sample sizes. Furthermore, the Bayesian methods with informative priors performed better (smaller values of deviance information criterion (DIC)) than those with the non-informative priors. Therefore, our results demonstrated the advantages of applying the Bayesian methods on the SUR biomass models, not only obtaining better model fitting and predictions, but also offering the assessment and evaluation of the uncertainties for constructing and updating tree biomass models.

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Seemingly Unrelated Regression and the Demand for Automobiles of Different Sizes, 1965-75: A Disaggregate Approach

The Journal of Business ◽

10.1086/295996 ◽

1978 ◽

Vol 51 (2) ◽

pp. 243 ◽

Cited By ~ 30

Author(s):

Rodney L. Carlson

Keyword(s):

Seemingly Unrelated Regression ◽

Unrelated Regression

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Arbitrage Pricing Theory as a Restricted Nonlinear Multivariate Regression Model: Iterated Nonlinear Seemingly Unrelated Regression Estimates

Journal of Business and Economic Statistics ◽

10.2307/1391415 ◽

1988 ◽

Vol 6 (1) ◽

pp. 29 ◽

Cited By ~ 54

Author(s):

Marjorie B. McElroy ◽

Edwin Burmeister

Keyword(s):

Regression Model ◽

Multivariate Regression ◽

Seemingly Unrelated Regression ◽

Arbitrage Pricing Theory ◽

Arbitrage Pricing ◽

Multivariate Regression Model ◽

Unrelated Regression ◽

Pricing Theory

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A Note on the Efficiency of Seemingly Unrelated Regression

The American Statistician ◽

10.1080/00031305.1988.10475545 ◽

1988 ◽

Vol 42 (2) ◽

pp. 137-139 ◽

Cited By ~ 2

Author(s):

James K. Binkley ◽

Carl H. Nelson

Keyword(s):

Seemingly Unrelated Regression ◽

Unrelated Regression

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Workplace disability and job satisfaction in Britain: A co-worker test?

Economic and Industrial Democracy ◽

10.1177/0143831x211014258 ◽

2021 ◽

pp. 0143831X2110142

Author(s):

Getinet Astatike Haile

Keyword(s):

Job Satisfaction ◽

Negative Relationship ◽

Organisational Culture ◽

Seemingly Unrelated Regression ◽

Unrelated Regression ◽

Significant Negative Relationship ◽

Using Data

The article examines the link between workplace disability (WD) and workplace job satisfaction (JS) using data from WERS2011. Controlling for a rich set of workplace characteristics including organisational culture, the study finds a significant negative relationship between JS and the share of disabled respondents within workplaces. Notably, Seemingly Unrelated Regression (SUR)-based analysis distinguishing between disabled and non-disabled respondents reveals that the negative relationship found is specific to non-disabled respondents. Moreover, disability equality policies are found to be significantly positively related with disabled respondents’ JS while they are negatively related with the JS of their non-disabled counterparts. The article ponders if there is a co-worker aspect to the WD–JS link and whether HR policies may need to take heed of co-worker dynamics in this respect.

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Determinan Kelaparan di Indonesia Tahun 2015-2019

Seminar Nasional Official Statistics ◽

10.34123/semnasoffstat.v2021i1.962 ◽

2021 ◽

Vol 2021 (1) ◽

pp. 547-556

Author(s):

Daniel M V Mone ◽

Efri Diah Utami

Keyword(s):

Sustainable Development ◽

Sustainable Development Goals ◽

Fixed Effect Model ◽

Fixed Effect ◽

Seemingly Unrelated Regression ◽

Unrelated Regression ◽

Effect Model ◽

Development Goals

Sustainable Development Goals (SDGs) adalah sebuah perencana aksi berskala global yang disepakati oleh para pemimpin dunia, termasuk Indonesia dengan tujuan mendorong pembangunan sosial, ekonomi dan lingkungan hidup. Salah satu dari 17 tujuan SDGs adalah mengakhiri kelaparan. Berdasarkan data yang dirilis Badan Pusat Statistik, salah satu pendekatan untuk mengukur tingkat kelaparan adalah proporsi penduduk dengan asupan kalori minimum di bawah 1400 kkal/kapita/hari. Proporsi penduduk dengan asupan kalori minimum di bawah 1400 kkal/kapita/hari di Indonesia masih cukup tinggi dan terus mengalami peningkatan dari tahun 2017 hingga 2019. Penelitian ini bertujuan untuk menganalisis bagaimana gambaran umum dari tingkat kelaparan dan variabel-variabel yang diduga mempengaruhinya, serta bagaimana pengaruh variabel-variabel tersebut terhadap tingkat kelaparan di Indonesia tahun 2015-2019. Hasil dari penelitian ini dapat digunakan untuk merumuskan kebijakan-kebijakan guna penuntasan kelaparan di Indonesia. Metode analisis yang digunakan adalah regresi data panel dengan menggunakan fixed effect model yang diestimasi dengan metode Seemingly Unrelated Regression (SUR). Hasil dari penelitian ini menunjukkan bahwa variabel yang berpengaruh signifikan terhadap tingkat kelaparan adalah pengeluaran makanan dan harga beras, sedangkan jumlah penduduk miskin dan pendapatan perkapita tidak berpengaruh signifikan.

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