scholarly journals Modeling and mapping wildfire ignition risk in Portugal

2009 ◽  
Vol 18 (8) ◽  
pp. 921 ◽  
Author(s):  
Filipe X. Catry ◽  
Francisco C. Rego ◽  
Fernando L. Bação ◽  
Francisco Moreira
Keyword(s):  
Spatial Patterns ◽  
Regression Models ◽  
Good Accuracy ◽  
National Level ◽  
Risk Map ◽  
Wildfire Management ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Fire Ignitions ◽  
Southern European Countries

Portugal has the highest density of wildfire ignitions among southern European countries. The ability to predict the spatial patterns of ignitions constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. In this study, we analyzed 127 490 ignitions that occurred in Portugal during a 5-year period. We used logistic regression models to predict the likelihood of ignition occurrence, using a set of potentially explanatory variables, and produced an ignition risk map for the Portuguese mainland. Results show that population density, human accessibility, land cover and elevation are important determinants of spatial distribution of fire ignitions. In this paper, we demonstrate that it is possible to predict the spatial patterns of ignitions at the national level with good accuracy and using a small number of easily obtainable variables, which can be useful in decision-making for wildfire management.

scholarly journals Modelling Interaction Effects by Using Extended WOE Variables with Applications to Credit Scoring

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1903
Author(s):  
Carlos Giner-Baixauli ◽  
Juan Tinguaro Rodríguez ◽  
Alejandro Álvaro-Meca ◽  
Daniel Vélez
Keyword(s):  
Regression Models ◽  
Computational Study ◽  
Classification Tree ◽  
Credit Scoring ◽  
Logistic Models ◽  
Interaction Effects ◽  
Weight Of Evidence ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Decision Making Processes

The term credit scoring refers to the application of formal statistical tools to support or automate loan-issuing decision-making processes. One of the most extended methodologies for credit scoring include fitting logistic regression models by using WOE explanatory variables, which are obtained through the discretization of the original inputs by means of classification trees. However, this Weight of Evidence (WOE)-based methodology encounters some difficulties in order to model interactions between explanatory variables. In this paper, an extension of the WOE-based methodology for credit scoring is proposed that allows constructing a new kind of WOE variable devised to capture interaction effects. Particularly, these new WOE variables are obtained through the simultaneous discretization of pairs of explanatory variables in a single classification tree. Moreover, the proposed extension of the WOE-based methodology can be complemented as usual by balance scorecards, which enable explaining why individual loans are granted or not granted from the fitted logistic models. Such explainability of loan decisions is essential for credit scoring and even more so by taking into account the recent law developments, e.g., the European Union’s GDPR. An extensive computational study shows the feasibility of the proposed approach that also enables the improvement of the predicitve capability of the standard WOE-based methodology.

An investigation on fatality of drivers in vehicle–fixed object accidents on expressways in China: Using multinomial logistic regression model

2018 ◽  
Vol 232 (7) ◽  
pp. 643-654
Author(s):  
Yong Peng ◽  
Shuangling Peng ◽  
Xinghua Wang ◽  
Shiyang Tan
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Multinomial Logistic Regression ◽  
Hunan Province ◽  
Driving Experience ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Modeling Process ◽  
Best Fitting ◽  
Bad Weather

This study aims to identify the effects of characteristics of vehicle, roadway, driver, and environment on fatality of drivers in vehicle-fixed object accidents on expressways in Changsha–Zhuzhou–Xiangtan district of Hunan province in China by developing multinomial logistic regression models. For this purpose, 121 vehicle–fixed object accidents from 2011-2017 are included in the modeling process. First, descriptive statistical analysis is made to understand the main characteristics of the vehicle–fixed object crashes. Then, 19 explanatory variables are selected, and correlation analysis of each two variables is conducted to choose the variables to be concluded. Finally, five multinomial logistic regression models including different independent variables are compared, and the model with best fitting and prediction capability is chosen as the final model. The results showed that the turning direction in avoiding fixed objects raised the possibility that drivers would die. About 64% of drivers died in the accident were found being ejected out of the car, of which 50% did not use a seatbelt before the fatal accidents. Drivers are likely to die when they encounter bad weather on the expressway. Drivers with less than 10 years of driving experience are more likely to die in these accidents. Fatigue or distracted driving is also a significant factor in fatality of drivers. Findings from this research provide an insight into reducing fatality of drivers in vehicle–fixed object accidents.

scholarly journals How to Test for Goodness of Fit in Ordinal Logistic Regression Models

2017 ◽  
Vol 17 (3) ◽  
pp. 668-686 ◽  
Author(s):  
Morten W. Fagerland ◽  
David W. Hosmer
Keyword(s):  
Regression Models ◽  
Goodness Of Fit ◽  
Logistic Models ◽  
Ratio Test ◽  
Response Variable ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Goodness Of Fit Tests ◽  
Interaction Terms ◽  
Different Types

Ordinal regression models are used to describe the relationship between an ordered categorical response variable and one or more explanatory variables. Several ordinal logistic models are available in Stata, such as the proportional odds, adjacent-category, and constrained continuation-ratio models. In this article, we present a command (ologitgof) that calculates four goodness-of-fit tests for assessing the overall adequacy of these models. These tests include an ordinal version of the Hosmer–Lemeshow test, the Pulkstenis–Robinson chi-squared and deviance tests, and the Lipsitz likelihood-ratio test. Together, these tests can detect several different types of lack of fit, including wrongly specified continuous terms, omission of different types of interaction terms, and an unordered response variable.

scholarly journals Determinants of Postponed Dental Visits Due to Costs: Evidence from the Survey of Health, Ageing, and Retirement in Germany

2019 ◽  
Vol 16 (18) ◽  
pp. 3344 ◽  
Author(s):  
Ghazal Aarabi ◽  
Richelle Valdez ◽  
Kristin Spinler ◽  
Carolin Walther ◽  
Udo Seedorf ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Regression Models ◽  
Outcome Measure ◽  
Well Being ◽  
Older Individuals ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Dental Visits ◽  
Health Related

High costs are an important reason patients postpone dental visits, which can lead to serious medical consequences. However, little is known about the determinants of postponing visits due to financial constraints longitudinally. Thus, the purpose of this study was to examine the determinants of postponing dental visits due to costs in older adults in Germany longitudinally. Data from wave 5 and 6 of the Survey of Health, Ageing, and Retirement in Europe was used. The occurrence of postponed dental visits due to costs in the last 12 months served as the outcome measure. Socioeconomic and health-related explanatory variables were included. Conditional fixed effects logistic regression models were used (n = 362). Regressions showed that the likelihood of postponing dental visits due to costs increased with lower age, less chronic disease, and lower income. The outcome measure was neither associated with marital status nor self-rated health. Identifying the factors associated with postponed dental visits due to costs might help to mitigate this challenge. In the long term, this might help to maintain the well-being of older individuals.

Longevity and dynamics of fatally and nonfatally topped Douglas-fir in the Coast Range of Oregon

2009 ◽  
Vol 39 (11) ◽  
pp. 2224-2233 ◽  
Author(s):  
Tristan D. Huff ◽  
John D. Bailey
Keyword(s):  
Pseudotsuga Menziesii ◽  
Group Selection ◽  
Regression Models ◽  
Forest Ecosystems ◽  
Structural Heterogeneity ◽  
Douglas Fir ◽  
Coast Range ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Ground Slope

Worldwide, snags are an important, but often lacking, component of forest ecosystems. We revisited artificially topped Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) trees 16–18 years after treatment in a replicated experiment in western Oregon. Some trees had been topped such that no live crown was retained (fatally topped), while others retained some portion of their live crown after topping (nonfatally topped). Topped trees were created under three different silvicultural regimes: clearcut, two story, and group selection. Twenty-three percent (61 of 262) of nonfatally topped trees remained living 16–18 years after treatment; 4% (19 of 482) of fatally topped trees had broken at some point up the bole by 16–18 years after treatment. Silvicultural regime, post-treatment height, stem diameter, stem lean, and ground slope were considered as potential explanatory variables in logistic regression models explaining mortality and breakage. A nonfatally topped tree’s odds of surviving 16–18 years after treatment was greater in the mature matrix of group selection stands than in clearcuts or two-story stands. A fatally topped tree’s odds of breaking within 16–18 years of treatment decreased as DBH increased. If carefully created, artificially topping trees can be a useful silvicultural tool to increase structural heterogeneity.

6. Regression Models with Parametrically Weighted Explanatory Variables

2002 ◽  
Vol 32 (1) ◽  
pp. 219-245 ◽  
Author(s):  
Kazuo Yamaguchi
Keyword(s):  
Employment Status ◽  
Regression Models ◽  
Working Women ◽  
Sociological Research ◽  
Occupational Prestige ◽  
Full Time ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Part Time

This paper describes linear regression models with parametrically weighted explanatory variables and related logistic regression models that estimate parameters characterizing (1) the effects of weighted variables on the dependent variable and (2) weights for the components of weighted variables. The models also characterize parsimoniously the interaction effects between weighted variables and covariates on the dependent variable by the use of various constraints on parameters. In particular, the models are concerned with testing the significance of variation with covariates in the weights of weighted variables separately from the significance of variation with those covariates in the effects of weighted variables. The usefulness of these models in sociological research is demonstrated by an illustrative analysis of the class identifications of married working women using education, occupational prestige, and income as three variables weighted between own and spousal attributes, and using year, age, race, part-time–full-time distinction, and employment status as covariates.

Dealing with Separation in Logistic Regression Models

2016 ◽  
Vol 24 (3) ◽  
pp. 339-355 ◽  
Author(s):  
Carlisle Rainey
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Prior Distribution ◽  
Regression Models ◽  
Prior Information ◽  
Jeffreys Prior ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Point Estimates ◽  
Quantities Of Interest

When facing small numbers of observations or rare events, political scientists often encounter separation, in which explanatory variables perfectly predict binary events or nonevents. In this situation, maximum likelihood provides implausible estimates and the researcher might want incorporate some form of prior information into the model. The most sophisticated research uses Jeffreys’ invariant prior to stabilize the estimates. While Jeffreys’ prior has the advantage of being automatic, I show that it often provides too much prior information, producing smaller point estimates and narrower confidence intervals than even highly skeptical priors. To help researchers assess the amount of information injected by the prior distribution, I introduce the concept of a partial prior distribution and develop the tools required to compute the partial prior distribution of quantities of interest, estimate the subsequent model, and summarize the results.

scholarly journals Contribution of human and biophysical factors to the spatial distribution of forest fire ignitions and large wildfires in a French Mediterranean region

2017 ◽  
Vol 26 (6) ◽  
pp. 498 ◽  
Author(s):  
Julien Ruffault ◽  
Florent Mouillot
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Mediterranean Region ◽  
Fire Hazard ◽  
Regional Distribution ◽  
Boosted Regression Trees ◽  
Fire Weather ◽  
Explanatory Variables ◽  
Management Actions ◽  
Fire Ignitions

Identifying the factors that drive the spatial distribution of fires is one of the most challenging issues facing fire science in a changing world. We investigated the relative influence of humans, land cover and weather on the regional distribution of fires in a Mediterranean region using boosted regression trees and a set of seven explanatory variables. The spatial pattern of fire weather, which is seldom accounted for in regional models, was estimated using a semi-mechanistic approach and expressed as the length of the fire weather season. We found that the drivers of the spatial distribution of fires followed a fire size-dependent pattern in which human activities and settlements mainly determined the distribution of all fires whereas the continuity and type of fuels mainly controlled the location of the largest fires. The spatial structure of fire weather was estimated to be responsible for an average of 25% of the spatial patterns of fires, suggesting that climate change may directly affect the spatial patterns of fire hazard in the near future. These results enhance our understanding of long-term controls of the spatial distribution of wildfires and predictive maps of fire hazard provide useful information for fire management actions.

Asymmetry in Protest Control? Comparing Protest Policing Patterns in Montreal, Toronto, And Vancouver, 1998-2004

2010 ◽  
Vol 15 (4) ◽  
pp. 489-509 ◽  
Author(s):  
Patrick Rafail
Keyword(s):  
Regression Models ◽  
Management Practices ◽  
National Level ◽  
Management Model ◽  
Logistic Regression Models ◽  
Cooperative Relationships ◽  
Protest Policing ◽  
The 1960S ◽  
Bayesian Logistic Regression ◽  
Western Democracies

Scholars have argued that since the 1960s, protest policing in Western democracies has moved toward emphasizing cooperative relationships with challenging groups. This evolution is referred to as the negotiated management model of protest control. Much of the literature that informs this perspective is based on either analyses of a limited subset of demonstrations or from national-level observation. Few studies have examined whether negotiated management practices hold at city levels. This research examines city-level protest policing using 1,152 demonstrations occurring between 1998 and 2004 in Montreal, Toronto, and Vancouver, Canada. Bayesian logistic regression models are estimated using arrests as the response variable. The main findings suggest that only protestor premobilization and the police use of force are uniformly related to arrests; that there is considerable variation across cities; and that the larger pattern of results is not consistent with negotiated management practices.

scholarly journals On Group Comparisons with Logistic Regression Models

2017 ◽  
Author(s):  
Jouni Kuha ◽  
Colin Mills
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Causal Effects ◽  
Binary Response ◽  
Binary Responses ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Continuous Response ◽  
Binary Model ◽  
The Subject

It is widely believed that regression models for binary responses are problematic if we want to compare estimated coefficients from models for different groups or with different explanatory variables. This concern has two forms. The first arises if the binary model is treated as an estimate of a model for an unobserved continuous response, and the second when models are compared between groups which have different distributions of other causes of the binary response. We argue that these concerns are usually misplaced. The first of them is only relevant if the unobserved continuous response is really the subject of substantive interest. If it is, the problem should be addressed through better measurement of this response. The second concern refers to a situation which is unavoidable but unproblematic, in that causal effects and descriptive associations are inherently group-dependent and can be compared as long as they are correctly estimated.

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