The spatio-temporal generalized additive model for criminal incidents

2011 ◽  
Author(s):  
Xiaofeng Wang ◽  
Donald E. Brown
Keyword(s):  
Generalized Additive Model ◽  
Additive Model ◽  
Spatio Temporal

scholarly journals Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

Risks ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 53
Author(s):  
Yves Staudt ◽  
Joël Wagner
Keyword(s):  
Random Forests ◽  
Goodness Of Fit ◽  
Generalized Additive Model ◽  
Mean Squared Error ◽  
Additive Model ◽  
Continuous Variables ◽  
Insurance Portfolio ◽  
Normal Transformation ◽  
Car Insurance ◽  
Log Normal

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance.

scholarly journals Generalized additive model with embedded variable selection for bankruptcy prediction: Prediction versus interpretation

2019 ◽  
Vol 7 (1) ◽  
pp. 1597956
Author(s):  
Carlos Valencia ◽  
Sergio Cabrales ◽  
Laura Garcia ◽  
Juan Ramirez ◽  
Diego Calderona ◽  
...  
Keyword(s):  
Variable Selection ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Bankruptcy Prediction ◽  
Selection For

scholarly journals Deoxygenation impacts on Baltic Sea cod: Dramatic declines in ecosystem services of an iconic keystone predator

AMBIO ◽  
2021 ◽  
Author(s):  
Alessandro Orio ◽  
Yvette Heimbrand ◽  
Karin Limburg
Keyword(s):  
Ecosystem Services ◽  
Baltic Sea ◽  
Gadus Morhua ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Maximum Length ◽  
The Baltic Sea ◽  
Hypoxic Zone ◽  
The Baltic ◽  
Baltic Cod

AbstractThe intensified expansion of the Baltic Sea’s hypoxic zone has been proposed as one reason for the current poor status of cod (Gadus morhua) in the Baltic Sea, with repercussions throughout the food web and on ecosystem services. We examined the links between increased hypoxic areas and the decline in maximum length of Baltic cod, a demographic proxy for services generation. We analysed the effect of different predictors on maximum length of Baltic cod during 1978–2014 using a generalized additive model. The extent of minimally suitable areas for cod (oxygen concentration ≥ 1 ml l−1) is the most important predictor of decreased cod maximum length. We also show, with simulations, the potential for Baltic cod to increase its maximum length if hypoxic areal extent is reduced to levels comparable to the beginning of the 1990s. We discuss our findings in relation to ecosystem services affected by the decrease of cod maximum length.

A Scientometric Study of Foraminiferal Research in Brazil

2021 ◽  
Vol 51 (4) ◽  
pp. 267-285
Author(s):  
Beatriz Lima Vieira ◽  
Letícia Rizzetto Patrocínio ◽  
Douglas Villela de Oliveira Lessa ◽  
Doriedson Ferreira Gomes
Keyword(s):  
Continental Margin ◽  
Generalized Additive Model ◽  
Scientific Literature ◽  
Additive Model ◽  
Scientific Production ◽  
Field Of Study ◽  
The North ◽  
Marine Realm ◽  
Scientometric Study ◽  
Brazilian Science

ABSTRACT Scientometrics is a field of study that involves measuring and analyzing scientific literature and can be a valuable tool to assess and reveal major gaps in national scientific production. Among the major challenges for Brazilian science is the development of research in the extensive national marine realm. This paper provides a scientometric survey of papers involving foraminiferal research in Brazil. The metrics utilized were papers listed in “Capes Portal” and “Scopus” databases up to the year of 2019. A total of 324 papers were found and 177 were selected based upon criteria established. A generalized additive model (GAM) was used to establish a relationship between publications and time. Studies involving foraminifera increased in Brazil from 1952 to 2019. Most studies have been conducted in the southeast region. We identified the need for more research on foraminifera to be carried out in the Brazilian continental margin, especially in the north and northeast regions of the country.

Integrating multiple data sources in match-fixing warning systems

2018 ◽  
Vol 18 (5-6) ◽  
pp. 483-504 ◽  
Author(s):  
Marius Ötting ◽  
Roland Langrock ◽  
Christian Deutscher
Keyword(s):  
Generalized Additive Model ◽  
Additive Model ◽  
Ground Truth ◽  
Warning Systems ◽  
Volume Data ◽  
Detection Systems ◽  
Multiple Data ◽  
Log Normal ◽  
Betting Odds

Recent years have seen several match-fixing scandals in soccer. In order to avoid match-fixing, existing literature and fraud detection systems primarily focus on analysing betting odds provided by bookmakers. In our work, we suggest to not only analyse odds but also total volume placed on bets, thereby making use of more of the information available. As a case study for our method, we consider the second division in Italian soccer, Serie B, since for this league it has effectively been proven that some matches were fixed, such that to some extent we can ground truth our approach. For the betting volume data, we use a flexible generalized additive model for location, scale and shape (GAMLSS), with log-normal response, to account for the various complex patterns present in the data. For the betting odds, we use a GAMLSS with bivariate Poisson response to model the number of goals scored by both teams, and to subsequently derive the corresponding odds. We then conduct outlier detection in order to flag suspicious matches. Our results indicate that monitoring both betting volumes and betting odds can lead to more reliable detection of suspicious matches.

scholarly journals Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal

2018 ◽  
Vol 7 (7) ◽  
pp. 275 ◽  
Author(s):  
Bipin Acharya ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Laxman Khanal ◽  
Shahid Naeem ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Dengue Fever ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Environmental Variables ◽  
Vegetation Index ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Remotely Sensed ◽  
Cross Correlation Analysis

Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

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