Interpretation of sugar maple (Acersaccharum) ring chronologies from central and southern Ontario using a mixed linear model

1994 ◽  
Vol 24 (3) ◽  
pp. 568-575 ◽  
Author(s):  
D.A.J. Ryan ◽  
O.B. Allen ◽  
D.L. McLaughlin ◽  
A.M. Gordon
Keyword(s):  
Air Pollution ◽  
Linear Model ◽  
Fixed Effects ◽  
Sugar Maple ◽  
Mixed Linear Model ◽  
Tree Age ◽  
Categorical Variables ◽  
Natural Logarithm ◽  
The Impact ◽  
Precipitation And Temperature

This study presents a new method for assessing the impact of environmental factors on the radial growth rate of trees. The natural logarithm of the specific volume increments (SVI) were fitted to a mixed linear model, which included fixed effects for tree age when the increment occurs, year, precipitation, and temperature both in the year of growth and in the preceding year, and the geographical locale. The model also incorporates stand and tree as random effects. By fitting trees of different ages, the model is able to separate year effects from age effects. Age and year were treated as categorical variables and hence no specific form of growth curve is assumed. The model was fitted to log SVI from 84 mature sugar maple (Acersaccharum Marsh.) trees from 42 uneven-aged stands in six regions of southern and central Ontario representing a known gradient of air pollution. After adjusting for age, precipitation, and temperature effects, the log SVI increased during the first half and declined during the second half of the 20th century in five of the six regions. This suggests that factors other than those included in the model have affected growth. Air pollution remains a likely causal agent in this observed decline.

Interpretation of sugar maple (Acersaccharum) ring chronologies from central and southern Ontario using a mixed linear model

1994 ◽  
Vol 24 (10) ◽  
pp. 2139-2139 ◽  
Author(s):  
D.A.J. Ryan ◽  
O.B. Allen ◽  
D.L. McLaughlin ◽  
A.M. Gordon
Keyword(s):  
Linear Model ◽  
Sugar Maple ◽  
Mixed Linear Model ◽  
Southern Ontario

not available

scholarly journals On modeling and analyzing barley malt data in different years

2019 ◽  
Vol 56 (1) ◽  
pp. 45-57
Author(s):  
Iwona Mejza ◽  
Katarzyna Ambroży-Deręgowska ◽  
Jan Bocianowski ◽  
Józef Błażewicz ◽  
Marek Liszewski ◽  
...  
Keyword(s):  
Linear Model ◽  
Random Effects ◽  
Fixed Effects ◽  
Linear Models ◽  
Model Fitting ◽  
Mixed Linear Model ◽  
Barley Grain ◽  
Barley Malt ◽  
Starting Point ◽  
Quality Coefficient

SummaryThe main purpose of this study was the model fitting of data deriving from a three-year experiment with barley malt. Two linear models were considered: a fixed linear model with fixed effects of years and other factors, and a mixed linear model with random effects of years and fixed effects of other factors. Two cultivars of brewing barley, Sebastian and Mauritia, six methods of nitrogen fertilization and four germination times were analyzed. Three quantitative traits were observed: practical extractivity of the malt, malting productivity, and a quality coefficient Q. The starting point for the statistical analyses was the available experimental material, which consisted of barley grain samples destined for malting. The analyses were performed over a series of years with respect to fixed or random effects of years. Due to the strong differentiation of the years of the study and some significant interactions of factors with years, annual analyses were also carried out.

scholarly journals The Impact of Air Pollution on the Development of Inbound Tourism in Chinese Megacities: The Case Study of Beijing, Guangzhou and Chongqing

2021 ◽  
Author(s):  
Nianlin Zhou ◽  
Yeli Gu ◽  
Manyuan Jiang
Keyword(s):  
Air Pollution ◽  
Carbon Emissions ◽  
Fixed Effects ◽  
Negative Influence ◽  
Fixed Effects Model ◽  
Fixed Investment ◽  
Inbound Tourism ◽  
New Business ◽  
Negative Effect ◽  
The Impact

The existing studies pay more attention to the impact of public transport and other public service facilities on urban air pollution and tourism, but less on the negative effect of air pollution caused by carbon emissions of business fixed investment on inbound tourism. This article attempts to make a supplementary analysis about the above point through examining the correlation between air pollution associate with business fixed investment and the size of inbound tourism based on panel data of three megacities (Beijing, Guangzhou and Chongqing) in China over the period from 2015 to 2019. The findings of this paper show that the effects of air pollution linked with carbon emissions from business fixed investment on the number of inbound tourists (NIT) is a negative correlation, while the influence of GDP per capita and tourism revenue on NIT reveal a positive relationship by applying fixed effects model for benchmark regression and the system-GMM estimator for robustness check. Moreover, the negative influence of PM 10 on sample cities is more than PM2.5. Some different results of core variables between benchmark and sub-sample regressions don’t imply the above conclusion to be substantively changed because of different distribution and concentration of nominal inbound tourists in specific sample megacities. In order to fundamentally improve air quality and to stimulate the development of inbound tourism, the suggestion of this study is to promote new business fixed investment with clean energy of renewable and low carbon.

The hazards of PM2.5 pollution: Empirical evidence from perinatal mortality in China

2020 ◽  
Author(s):  
guangqin Li ◽  
Lingyu Li ◽  
Dan Liu
Keyword(s):  
Air Pollution ◽  
Perinatal Mortality ◽  
Spatial Econometrics ◽  
Fixed Effects ◽  
Mortality Rates ◽  
Variable Method ◽  
Haze Pollution ◽  
Instrument Variable ◽  
The Impact ◽  
Positive Effect

Abstract Background : Although the adverse effects of air pollution on health have aroused widespread concern in academia, there is little evidence about the impact of PM2.5 on perinatal mortality rates. Methods: Using the spatial analysis function of ArcGIS, we get the haze pollution data from the satellite remote sensing data. We adopt fixed effects model, spatial Durbin model (SDM) and the instrument variable method to investigate the causality between PM2.5 and perinatal mortality rates. Results: We find that PM2.5 has a significantly positive effect on perinatal mortality rates. A 1% increase of log-transformed average concentrations and maximum concentrations of PM2.5 result in 1.76‰ and 2.31‰ increase of perinatal mortality rates, respectively. In spatial econometrics analysis, we find PM2.5 has significant spatial autocorrelation characteristics. A 1% increase of concentrations of log-transformed average and maximum PM2.5 lead to a 2.49‰ and 2.19‰ increase of perinatal mortality rates, respectively. Using instrument variable method to deal with the endogeneity, the result is similar. The potential mechanism through which air pollution has an impact on perinatal mortality rates is infant weight. Conclusions: PM2.5 pollution has a significant and positive effect on perinatal mortality. The results show that environmental pollution control should be strengthened and the exposure of pregnant women in polluted air should be reduced.

scholarly journals Fatores de Impacto sobre o Saldo de Caixa: Um Estudo em Empresas Brasileiras Não Financeiras de Capital Aberto

2016 ◽  
Vol 14 (1) ◽  
pp. 125
Author(s):  
Júlia Peres Tortoli ◽  
Marcelo Botelho da Costa Moraes
Keyword(s):  
Financial Crisis ◽  
Financial Management ◽  
Fixed Effects ◽  
Control Variable ◽  
Impact Factors ◽  
Dummy Variable ◽  
Natural Logarithm ◽  
Net Assets ◽  
The Impact ◽  
Corporate Cash Holdings

The aim of this study is to analyze the impact factors and their effects on the corporate cash holdings in order to assist companies in achieving better financial management, corroborating the perpetuity of them. The sample consists of 917 observations of 131 listed companies in Brazil, from 2007 to 2013. The dependent variable used in this study is the natural logarithm of cash and cash equivalents divided by the total of net assets and the major independent variables are show in literature plus a dummy variable for passive financing through BNDES and a control variable for the financial crisis. The methodology used is regression with static and balanced panel data, with better results for fixed effects. Evidence is found that the variable distribution of dividends, higher level of liquid assets, existence of corporate governance, in addition to financial crisis, impact cash.

scholarly journals Improving Estimates of Fixed Effects in a Mixed Linear Model

1991 ◽  
Vol 74 (9) ◽  
pp. 3174-3182 ◽  
Author(s):  
K.A. Weigel ◽  
D. Gianola ◽  
R.J. Tempelman ◽  
C.A. Matos ◽  
I.H.C. Chen ◽  
...  
Keyword(s):  
Linear Model ◽  
Fixed Effects ◽  
Mixed Linear Model

scholarly journals Thermoregulation mode, substrate temperature and camouflage efficiency affecting defensive behavior of lizards in Amazonia

2020 ◽  
Vol 50 (4) ◽  
pp. 339-345
Author(s):  
Erika SOUZA ◽  
Ana COELHO ◽  
Alfredo P. SANTOS-JR ◽  
Ricardo Alexandre KAWASHITA-RIBEIRO ◽  
Rafael de FRAGA
Keyword(s):  
Linear Model ◽  
Fixed Effects ◽  
Cost Benefit ◽  
Mixed Linear Model ◽  
Mixed Effects Models ◽  
Random Factor ◽  
High Exposure ◽  
Defensive Responses ◽  
Approach Distance ◽  
Model Temperature

ABSTRACT In ectotherms, defensive responses to predators usually depend on cost-benefit relationships between death risk and the energy required to flee. In this study we investigate Amazonian lizards to test the hypothesis that the minimum predator approach distance (PAD) is influenced by temperature and camouflage. We test the hypothesis that PAD estimated for species with different thermoregulation modes respond differently to temperature and camouflage. We sampled 35 lizards of a heliotherm and a non-heliotherm species, for which we simulated a terrestrial visually oriented predator. Using a fixed-effects linear model, temperature positively affected PAD estimates, but the camouflage did not contribute to the model. Using a mixed linear model assuming thermoregulation mode as a random factor, camouflage negatively affected PAD estimates, independently of temperature. Our findings suggest that high exposure to predators in open habitats may be compensated by rapid fleeing optimized by high temperatures, and low fleeing performance, usually caused by relatively low temperatures in shaded habitats, may be compensated by camouflage. However, identifying the best PAD predictor greatly depended on accounting for thermoregulation mode in hypothesis testing, although the results obtained by both fixed and mixed-effects models may be relevant for conservation.

scholarly journals Optimizing the identification of causal variants across varying genetic architectures in crops

2018 ◽  
Author(s):  
Chenyong Miao ◽  
Jinliang Yang ◽  
James C. Schnable
Keyword(s):  
Linear Model ◽  
Complex Traits ◽  
Statistical Power ◽  
Linear Models ◽  
Association Studies ◽  
Mixed Linear Model ◽  
Frequency Distributions ◽  
Model Based ◽  
A Genome ◽  
The Impact

AbstractBackgroundAssociation studies use statistical links between genetic markers and variation in a phenotype’s value across many individuals to identify genes controlling variation in the target phenotype. However, this approach, particularly conducted on a genome-wide scale (GWAS), has limited power to identify the genes responsible for variation in traits controlled by complex genetic architectures.ResultsHere we employ simulation studies utilizing real-world genotype datasets from association populations in four species with distinct minor allele frequency distributions, population structures, and patterns linkage disequilibrium to evaluate the impact of variation in both heritability and trait complexity on both conventional mixed linear model based GWAS and two new approaches specifically developed for complex traits. Mixed linear model based GWAS rapidly losses power for more complex traits. FarmCPU, a method based on multi-locus mixed linear models, provides the greatest statistical power for moderately complex traits. A Bayesian approach adopted from genomic prediction provides the greatest statistical power to identify causal genetic loci for extremely complex traits.ConclusionsUsing estimates of the complexity of the genetic architecture of target traits can guide the selection of appropriate statistical methods and improve the overall accuracy and power of GWAS.

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