scholarly journals Methods for estimating aboveground biomass and its components for Douglas-fir and lodgepole pine trees

2016 ◽  
Vol 46 (1) ◽  
pp. 77-87 ◽  
Author(s):  
K.P. Poudel ◽  
H. Temesgen
Keyword(s):  
Linear Regression ◽  
Aboveground Biomass ◽  
Mean Squared Error ◽  
Douglas Fir ◽  
Seemingly Unrelated Regression ◽  
Unrelated Regression ◽  
Pine Trees ◽  
Branch Biomass ◽  
Using Data ◽  
Log Linear

Estimating aboveground biomass and its components requires sound statistical formulation and evaluation. Using data collected from 55 destructively sampled trees in different parts of Oregon, we evaluated the performance of three groups of methods to estimate total aboveground biomass and (or) its components based on the bias and root mean squared error (RMSE) that they produced. The first group of methods used an analytical approach to estimate total and component biomass using existing equations and produced biased estimates for our dataset. The second group of methods used a system of equations fitted with seemingly unrelated regression (SUR) and were superior to the first group of methods in terms of bias and RMSE. The third group of methods predicted the proportion of biomass in each component using beta regression, Dirichlet regression, and multinomial log-linear regression. The predicted proportions were then applied to the total aboveground biomass to obtain the amount of biomass in each component. The multinomial log-linear regression approach consistently produced smaller RMSEs compared with both SUR methods. The beta and Dirichlet regressions were superior to both SUR methods except for Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) branch biomass, for which the simple SUR method produced smaller RMSE compared with the beta and Dirichlet regressions.

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

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.

scholarly journals Trends in Suicide Mortality by Method from 1979 to 2016 in Japan

2019 ◽  
Vol 16 (10) ◽  
pp. 1794 ◽  
Author(s):  
Bibha Dhungel ◽  
Maaya Kita Sugai ◽  
Stuart Gilmour
Keyword(s):  
Linear Regression ◽  
Linear Regression Analysis ◽  
Health Concern ◽  
Suicide Mortality ◽  
Registration System ◽  
Suicide Rates ◽  
Vital Registration System ◽  
Using Data ◽  
Age Adjusted Rates ◽  
Log Linear

Suicide is a major public health concern in Japan. This study aimed to characterize the trends in suicide mortality in Japan by method since 1979. Using data from the Japan vital registration system, we calculated age-standardized rates of suicide mortality separately by sex and method. We conducted a log-linear regression of suicide mortality rates separately by sex, and linear regression analysis of the proportion of deaths due to hanging, including a test for change in level and trend in 1998. While crude suicide rates were static over the time period, age-adjusted rates declined. The significant increase in suicide mortality in 1998 was primarily driven by large changes in the rate of hanging, with suicide deaths after 1998 having 36.7% higher odds of being due to hanging for men (95% CI: 16.3–60.8%), and 21.9% higher odds of being due to hanging for women (95% CI: 9.2–35.9%). Hanging has become an increasingly important method for committing suicide over the past 40 years, and although suicide rates have been declining continuously over this time, more effort is needed to prevent hanging and address the potential cultural drivers of suicide if the rate is to continue to decline in the future.

scholarly journals A New Liu Type of Estimator for the Restricted SUR Estimator

2020 ◽  
Vol 18 (1) ◽  
pp. 2-11
Author(s):  
Kristofer Månsson ◽  
B. M. Golam Kibria ◽  
Ghazi Shukur
Keyword(s):  
Linear Subspace ◽  
Mean Squared Error ◽  
Shrinkage Estimator ◽  
Seemingly Unrelated Regression ◽  
Simulation Techniques ◽  
Squared Error ◽  
Unrelated Regression ◽  
The Mean

A new Liu type of estimator for the seemingly unrelated regression (SUR) models is proposed that may be used when estimating the parameters vector in the presence of multicollinearity if the it is suspected to belong to a linear subspace. The dispersion matrices and the mean squared error (MSE) are derived. The new estimator may have a lower MSE than the traditional estimators. It was shown using simulation techniques the new shrinkage estimator outperforms the commonly used estimators in the presence of multicollinearity.

scholarly journals A Simulation Study of Bayesian Estimator for Seemingly Unrelated Regression under Different Distributional Assumptions

2021 ◽  
pp. 1-8
Author(s):  
Ojo O. Oluwadare ◽  
Owonipa R. Oluremi ◽  
Enesi O. Lateifat
Keyword(s):  
Normal Distribution ◽  
Simulation Study ◽  
Bayesian Method ◽  
Mean Squared Error ◽  
Classical Method ◽  
Seemingly Unrelated Regression ◽  
Bayesian Estimator ◽  
Squared Error ◽  
Unrelated Regression ◽  
Distributional Assumptions

This paper presents Bayesian analysis of Seemingly Unrelated Regression (SUR) model. An independent prior for parameters was used. The Bayesian method was compared with classical method of estimation to know the most efficient estimator under different distributional assumptions through a simulation study. In order to facilitate comparison among these estimators, Mean Squared Error (MSE) was considered as a criterion. Furthermore, based on the simulation, it was deduced that MSE of the Bayesian estimator is smaller than all the classical methods of estimation for SUR model while Normal distribution was considered as an ideal distribution  in generation of disturbances in any simulation study.

scholarly journals Some remarks on a pair of seemingly unrelated regression models

2019 ◽  
Vol 17 (1) ◽  
pp. 979-989 ◽  
Author(s):  
Jian Hou ◽  
Yong Zhao
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Sufficient Conditions ◽  
Seemingly Unrelated Regression ◽  
Regression Equations ◽  
Linear Regression Models ◽  
Unknown Parameters ◽  
Unrelated Regression ◽  
Best Linear Unbiased ◽  
Best Linear Unbiased Estimators

Abstract Linear regression models are foundation of current statistical theory and have been a prominent object of study in statistical data analysis and inference. A special class of linear regression models is called the seemingly unrelated regression models (SURMs) which allow correlated observations between different regression equations. In this article, we present a general approach to SURMs under some general assumptions, including establishing closed-form expressions of the best linear unbiased predictors (BLUPs) and the best linear unbiased estimators (BLUEs) of all unknown parameters in the models, establishing necessary and sufficient conditions for a family of equalities of the predictors and estimators under the single models and the combined model to hold. Some fundamental and valuable properties of the BLUPs and BLUEs under the SURM are also presented.

scholarly journals Simultaneous estimation as an alternative to young eucalyptus aboveground biomass modeling in ecophysiological experiments

2021 ◽  
Vol 43 ◽  
pp. e52126
Author(s):  
Thiago Wendling Gonçalves de Oliveira ◽  
Rafael Rubilar ◽  
Carlos Roberto Sanquetta ◽  
Ana Paula Dalla Corte ◽  
Alexandre Behling
Keyword(s):  
Least Squares ◽  
Aboveground Biomass ◽  
Forest Biomass ◽  
Seemingly Unrelated Regression ◽  
Biomass Estimation ◽  
Simultaneous Estimation ◽  
Efficiency Gain ◽  
Unrelated Regression ◽  
Biomass Components ◽  
Biomass Modeling

Accurate forest biomass estimates require the selection of appropriate models of individual trees. Thus, two properties are required in tree biomass modeling: (1) additivity of biomass components and (2) estimator efficiency. This study aimed to develop a system of equations to estimate young eucalyptus aboveground biomass and guarantee additivity and estimator efficiency. Aboveground eucalyptus biomass models were calibrated using four methods:  generalized least squares (GLS), weighted least squares (WLS), seemingly unrelated regression (SUR), and weighted seemingly unrelated regression (WSUR). The approaches were compared with regard to performance, additivity, and estimator efficiency. The methods did not differ with regard to the mean biomass estimation; therefore, their performance was similar. The GLS and WLS approaches did not satisfy the additivity principle, as the sum of the biomass components was not equal to total biomass. However, this was not observed with the SUR and WSUR approaches. With regard to estimator efficiency, the WSUR approach resulted in narrow confidence intervals and an efficiency gain of over 20%. The WSUR approach should be used in forest biomass modeling as it resulted in effective estimators while ensuring equation additivity, thus providing an easy and accurate alternative to estimate the initial biomass of eucalyptus stands in ecophysiological models.

Polar Magnetic Field Reversals of the Sun in Maunder Minimum

2000 ◽  
Vol 179 ◽  
pp. 193-196
Author(s):  
V. I. Makarov ◽  
A. G. Tlatov
Keyword(s):  
Magnetic Field ◽  
Maunder Minimum ◽  
The Sun ◽  
Annual Rings ◽  
Field Reversal ◽  
Polar Magnetic Field ◽  
Pine Trees ◽  
Using Data ◽  
Magnetic Field Reversal

AbstractA possible scenario of polar magnetic field reversal of the Sun during the Maunder Minimum (1645–1715) is discussed using data of magnetic field reversals of the Sun for 1880–1991 and the14Ccontent variations in the bi-annual rings of the pine-trees in 1600–1730 yrs.

scholarly journals Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 340
Author(s):  
Ewa Panek ◽  
Dariusz Gozdowski
Keyword(s):  
Linear Regression ◽  
Grain Yield ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Early Spring ◽  
European Countries ◽  
Yield Prediction ◽  
Country Level ◽  
Using Data

In this study, the relationships between normalized difference vegetation index (NDVI) obtained based on MODIS satellite data and grain yield of all cereals, wheat and barley at a country level were analyzed. The analysis was performed by using data from 2010–2018 for 20 European countries, where percentage of cereals is high (at least 35% of the arable land). The analysis was performed for each country separately and for all of the collected data together. The relationships between NDVI and cumulative NDVI (cNDVI) were analyzed by using linear regression. Relationships between NDVI in early spring and grain yield of cereals were very strong for Croatia, Czechia, Germany, Hungary, Latvia, Lithuania, Poland and Slovakia. This means that the yield prediction for these countries can be as far back as 4 months before the harvest. The increase of NDVI in early spring was related to the increase of grain yield by about 0.5–1.6 t/ha. The cumulative of averaged NDVI gives more stable prediction of grain yield per season. For France and Belgium, the relationships between NDVI and grain yield were very weak.

Educational Homogamy Among Married and Unmarried Couples in Europe

2008 ◽  
Vol 30 (1) ◽  
pp. 28-52 ◽  
Author(s):  
Dana Hamplova
Keyword(s):  
Linear Models ◽  
Married Couples ◽  
European Social Survey ◽  
Social Survey ◽  
Educational Homogamy ◽  
Educational Barriers ◽  
Educational Groups ◽  
Institutional Contexts ◽  
Using Data ◽  
Log Linear

In this article, educational homogamy among married and cohabiting couples in selected European countries is examined. Using data from two waves (2002 and 2004) of the European Social Survey, this article compares three cultural and institutional contexts that differ in terms of institutionalization of cohabitation. Evidence from log-linear models yields two main conclusions. First, as cohabitation becomes more common in society, marriage and cohabitation become more similar with respect to partner selection. Second, where married and unmarried unions differ in terms of educational homogamy, married couples have higher odds of overcoming educational barriers (i.e., intermarrying with other educational groups).

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