scholarly journals Modelling world energy security data from multinomial distribution by generalized linear model under different cumulative link functions

2018 ◽  
Vol 16 (1) ◽  
pp. 377-385
Author(s):  
Neslihan Iyit
Keyword(s):  
Linear Model ◽  
Energy Security ◽  
Generalized Linear Model ◽  
Multinomial Distribution ◽  
Energy Performance ◽  
Link Function ◽  
Response Variable ◽  
Ordinal Response ◽  
Link Functions ◽  
Cumulative Logit

AbstractEnergy securityis one of the major components of energy sustainability in the world’s energy performance. In this study,energy securityis taken as an ordinal response variable coming from the multinomial distribution with the energy grade levelsA,B,C, andD. Thereafter, the worldenergy securitydata is tried to be statistically modelled by usinggeneralized linear model (GLM)approach for the ordinal response variable under different cumulative link functions. The cumulative link functions comparatively used in this study are cumulative logit, cumulative probit, cumulative complementary log-log, cumulative Cauchit, and cumulative negative log-log. In order to avoid a multicollinearity problem in the data structure, principal component analysis (PCA) technique is integrated with theGLMapproach for the ordinal response variable. In this study, statistically, the importance of determining the best cumulative link function on the accuracy of parameter estimates, confidence intervals, and hypothesis tests in theGLMfor the multinomially distributed response variable is highlighted. In terms of energy evaluation, by usingcumulative logitas the best cumulative link function,energy sources consumptions,electricity productions from nuclear energy,natural gas,oil,coal,and hydroelectric,energy use per capita and energy importsare found to have statistically significant effects onenergy securityin the world’s energy performance.

scholarly journals Modeling dengue vector population with earth observation data and a generalized linear model

2021 ◽  
Author(s):  
Oladimeji Mudele ◽  
Alejandro Frery ◽  
Lucas FR Zanandrez ◽  
Alvaro E Eiras ◽  
Paolo Gamba
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Earth Observation ◽  
Environmental Data ◽  
Environmental Drivers ◽  
Support Vector ◽  
Mosquito Vector ◽  
Response Variable ◽  
Vector Population ◽  
Count Response

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population. Field campaigns are expensive, and data from meteorological sites that feed models with the required environmental data often lack detail. As a consequence, we explore temporal modeling of the population of Ae. aegypti mosquito vector species and environmental conditions- temperature, moisture, precipitation, and vegetation- have been shown to have significant effects. We use earth observation (EO) data as our source for estimating these biotic and abiotic environmental variables based on proxy features, namely: Normalized difference vegetation index, Normalized difference water index, Precipitation, and Land surface temperature. We obtained our response variable from field-collected mosquito population measured weekly using 791 mosquito traps in Vila Velha city, Brazil, for 36 weeks in 2017, and 40 weeks in 2018. Recent similar studies have used machine learning (ML) techniques for this task. However, these techniques are neither intuitive nor explainable from an operational point of view. As a result, we use a Generalized Linear Model (GLM) to model this relationship due to its fitness for count response variable modeling, its interpretability, and the ability to visualize the confidence intervals for all inferences. Also, to improve our model, we use the Akaike Information Criterion to select the most informative environmental features. Finally, we show how to improve the quality of the model by weighting our GLM. Our resulting weighted GLM compares well in quality with ML techniques: Random Forest and Support Vector Machines. These results provide an advancement with regards to qualitative and explainable epidemiological risk modeling in urban environments.

scholarly journals Arthropods on Abandoned Apple Trees: Comparison of Orchard Versus Alley

2016 ◽  
Vol 64 (2) ◽  
pp. 517-526 ◽  
Author(s):  
Václav Psota ◽  
Pavla Šťastná
Keyword(s):  
Czech Republic ◽  
Linear Model ◽  
Poisson Distribution ◽  
Malus Domestica ◽  
Generalized Linear Model ◽  
Wiener Index ◽  
Apple Orchard ◽  
Link Function ◽  
Apple Trees ◽  
South Moravia

Occurrence of arthropods on abandoned apple trees was studied in 2010 and 2011. The research was carried out in South Moravia (Czech Republic). Two sites were selected within this area – apple trees (Malus domestica) in an alley along a road and an abandoned apple orchard. At each location, arthropods were collected from 5 separate trees. Deltamethrin was applied into the treetops using a fogger. The killed arthropods were collected 15 minutes after the application. From among the collected data, 48 families were determined in accordance with a generalized linear model with a logarithmic-link function and Poisson distribution. As a result it was found that 33 families have significantly higher abundance in the abandoned orchard and 9 families in the alley. According to the Shannon-Wiener index, diversity of families was higher in the alley in both years (2010: H’ = 3.016, 2011: H’ = 3.177) compared to the abandoned orchard (2010: H’ = 2.413, 2011: H’ = 3.007).

scholarly journals Instantaneous Geometric Rates via Generalized Linear Models

2017 ◽  
Vol 17 (2) ◽  
pp. 358-371 ◽  
Author(s):  
Andrea Discacciati ◽  
Matteo Bottai
Keyword(s):  
Clinical Trial ◽  
Randomized Clinical Trial ◽  
Linear Model ◽  
Generalized Linear Models ◽  
Generalized Linear Model ◽  
Renal Carcinoma ◽  
Linear Models ◽  
Rate Model ◽  
Model Framework ◽  
Link Functions

The instantaneous geometric rate represents the instantaneous probability of an event of interest per unit of time. In this article, we propose a method to model the effect of covariates on the instantaneous geometric rate with two models: the proportional instantaneous geometric rate model and the proportional instantaneous geometric odds model. We show that these models can be fit within the generalized linear model framework by using two nonstandard link functions that we implement in the user-defined link programs log_igr and logit_igr. We illustrate how to fit these models and how to interpret the results with an example from a randomized clinical trial on survival in patients with metastatic renal carcinoma.

scholarly journals Asymptotics for a censored generalized linear model with unknown link function

2006 ◽  
Vol 138 (1-2) ◽  
pp. 235-267 ◽  
Author(s):  
Yanhua Wang ◽  
Shuyuan He ◽  
Lixing Zhu ◽  
Kam C. Yuen
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Link Function ◽  
Unknown Link

scholarly journals Modeling dengue vector population with earth observation data and a generalized linear model

2021 ◽  
Author(s):  
Oladimeji Mudele ◽  
Alejandro Frery ◽  
Lucas FR Zanandrez ◽  
Alvaro E Eiras ◽  
Paolo Gamba
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Earth Observation ◽  
Environmental Data ◽  
Environmental Drivers ◽  
Support Vector ◽  
Mosquito Vector ◽  
Response Variable ◽  
Vector Population ◽  
Count Response

Mosquitoes propagate many human diseases, some widespread and with no vaccines. The Ae. aegypti mosquito vector transmits Zika, Chikungunya, and Dengue viruses. Effective public health interventions to control the spread of these diseases and protect the population require models that explain the core environmental drivers of the vector population. Field campaigns are expensive, and data from meteorological sites that feed models with the required environmental data often lack detail. As a consequence, we explore temporal modeling of the population of Ae. aegypti mosquito vector species and environmental conditions- temperature, moisture, precipitation, and vegetation- have been shown to have significant effects. We use earth observation (EO) data as our source for estimating these biotic and abiotic environmental variables based on proxy features, namely: Normalized difference vegetation index, Normalized difference water index, Precipitation, and Land surface temperature. We obtained our response variable from field-collected mosquito population measured weekly using 791 mosquito traps in Vila Velha city, Brazil, for 36 weeks in 2017, and 40 weeks in 2018. Recent similar studies have used machine learning (ML) techniques for this task. However, these techniques are neither intuitive nor explainable from an operational point of view. As a result, we use a Generalized Linear Model (GLM) to model this relationship due to its fitness for count response variable modeling, its interpretability, and the ability to visualize the confidence intervals for all inferences. Also, to improve our model, we use the Akaike Information Criterion to select the most informative environmental features. Finally, we show how to improve the quality of the model by weighting our GLM. Our resulting weighted GLM compares well in quality with ML techniques: Random Forest and Support Vector Machines. These results provide an advancement with regards to qualitative and explainable epidemiological risk modeling in urban environments.

scholarly journals Nested Generalized Linear Model with Ordinal Response for Correlated Data

2012 ◽  
Vol 23 (2) ◽  
Author(s):  
Yekti Widyaningsih ◽  
Asep Saefuddin ◽  
Khairil A. Notodiputro ◽  
Aji H. Wigena
Keyword(s):  
Linear Model ◽  
Generalized Linear Model ◽  
Correlated Data ◽  
Ordinal Response
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