Hurdle models for multilevel zero-inflated data via h-likelihood

2010 ◽  
Vol 29 (30) ◽  
pp. 3294-3310 ◽  
Author(s):  
Marek Molas ◽  
Emmanuel Lesaffre
Keyword(s):  
Hurdle Models

scholarly journals A comparison of zero-inflated and hurdle models for modeling zero-inflated count data

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Cindy Xin Feng
Keyword(s):  
Health Services ◽  
Count Data ◽  
Goodness Of Fit ◽  
Negative Binomial ◽  
Simulation Studies ◽  
Final Choice ◽  
Hurdle Models ◽  
Count Distribution ◽  
Careful Assessment

AbstractCounts data with excessive zeros are frequently encountered in practice. For example, the number of health services visits often includes many zeros representing the patients with no utilization during a follow-up time. A common feature of this type of data is that the count measure tends to have excessive zero beyond a common count distribution can accommodate, such as Poisson or negative binomial. Zero-inflated or hurdle models are often used to fit such data. Despite the increasing popularity of ZI and hurdle models, there is still a lack of investigation of the fundamental differences between these two types of models. In this article, we reviewed the zero-inflated and hurdle models and highlighted their differences in terms of their data generating processes. We also conducted simulation studies to evaluate the performances of both types of models. The final choice of regression model should be made after a careful assessment of goodness of fit and should be tailored to a particular data in question.

scholarly journals Modeling fire ignition probability and frequency using Hurdle models: a cross-regional study in Southern Europe

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Marina D’Este ◽  
Antonio Ganga ◽  
Mario Elia ◽  
Raffaella Lovreglio ◽  
Vincenzo Giannico ◽  
...  
Keyword(s):  
Mediterranean Basin ◽  
Negative Binomial ◽  
Pearson Correlation ◽  
Fire Occurrence ◽  
Ignition Probability ◽  
Hurdle Models ◽  
Mediterranean Landscapes ◽  
The Mediterranean ◽  
Fire Ignition ◽  
The Mediterranean Basin

Abstract Background Wildfires play a key role in shaping Mediterranean landscapes and ecosystems and in impacting species dynamics. Numerous studies have investigated the wildfire occurrences and the influence of their drivers in many countries of the Mediterranean Basin. However, in this regard, no studies have attempted to compare different Mediterranean regions, which may appear similar under many aspects. In response to this gap, climatic, topographic, anthropic, and landscape drivers were analyzed and compared to assess the patterns of fire ignition points in terms of fire occurrence and frequency in Catalonia (Spain), Sardinia, and Apulia (Italy). Therefore, the objectives of the study were to (1) assess fire ignition occurrence in terms of probability and frequency, (2) compare the main drivers affecting fire occurrence, and (3) produce fire probability and frequency maps for each region. Results In pursuit of the above, the probability of fire ignition occurrence and frequency was mapped using Negative Binomial Hurdle models, while the models’ performances were evaluated using several metrics (AUC, prediction accuracy, RMSE, and the Pearson correlation coefficient). The results showed an inverse correlation between distance from infrastructures (i.e., urban roads and areas) and the occurrence of fires in all three study regions. This relationship became more significant when the frequency of fire ignition points was assessed. Moreover, a positive correlation was found between fire occurrence and landscape drivers according to region. The land cover classes more significantly affected were forest, agriculture, and grassland for Catalonia, Sardinia, and Apulia, respectively. Conclusions Compared to the climatic, topographic, and landscape drivers, anthropic activity significantly influences fire ignition and frequency in all three regions. When the distance from urban roads and areas decreases, the probability of fire ignition occurrence and frequency increases. Consequently, it is essential to implement long- to medium-term intervention plans to reduce the proximity between potential ignition points and fuels. In this perspective, the present study provides an applicable decision-making tool to improve wildfire prevention strategies at the European level in an area like the Mediterranean Basin where a profuse number of wildfires take place.

Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities

2021 ◽  
Vol 137 ◽  
pp. 538-554
Author(s):  
Pooja Sengupta ◽  
Baidyanath Biswas ◽  
Ajay Kumar ◽  
Ravi Shankar ◽  
Shivam Gupta
Keyword(s):  
Asia Pacific ◽  
Hurdle Models

Hurdle models of repayment behaviour in personal loan contracts

2016 ◽  
Vol 53 (2) ◽  
pp. 641-667
Author(s):  
José M. R. Murteira ◽  
Mário A. G. Augusto
Keyword(s):  
Loan Contracts ◽  
Hurdle Models

scholarly journals Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica

2021 ◽  
Author(s):  
Erin Bryce ◽  
Luigi Lombardo ◽  
Cees van Westen ◽  
Hakan Tanyas ◽  
Daniela Castro-Camilo
Keyword(s):  
Spatial Information ◽  
Landslide Hazard ◽  
Sufficient Information ◽  
Tropical Regions ◽  
Landslide Occurrence ◽  
Hurdle Models ◽  
Modelling Framework ◽  
Markovian Representation ◽  
Binary Classifiers ◽  
A Chain

Abstract Climatically-induced natural hazards are a threat to communities. They can cause life losses and heavy damage to infrastructure, and due to climate change, they have become increasingly frequent. This is especially in tropical regions, where major hurricanes have consistently appeared in recent history. Such events induce damage due to the high wind speed they carry, and the high intensity/duration rainfall they discharge can further induce a chain of hydro-morphological hazards in the form of widespread debris slides/flows. The way the scientific community has developed preparatory steps to mitigate the potential damage of these hydro-morphological threats includes assessing where they are likely to manifest across a given landscape. This concept is referred to as susceptibility, and it is commonly achieved by implementing binary classifiers to estimate probabilities of landslide occurrences. However, predicting where landslides can occur may not be sufficient information, for it fails to convey how large landslides may be. This work proposes using a flexible Bernoulli-log-Gaussian hurdle model to simultaneously model landslide occurrence and size per areal unit. Covariate and spatial information are introduced using a generalised additive modelling framework. To cope with the high spatial resolution of the data, our model uses a Markovian representation of the Matérn covariance function based on the stochastic partial differential equation (SPDE) approach. Assuming Gaussian priors, our model can be integrated into the class of latent Gaussian models, for which inference is conveniently performed based on the integrated nested Laplace approximation method. We use our modelling approach in Dominica, where Hurricane Maria (September 2017) induced thousands of shallow flow-like landslides passing over the island. Our results show that we can not only estimate where landslides may occur and how large they may be, but we can also combine this information in a unified landslide hazard model, which is the first of its kind.

scholarly journals Perceptions, Risk Attitude and Organic Fertilizer Investment: Evidence from Rice and Banana Farmers in Guangxi, China

2018 ◽  
Vol 10 (10) ◽  
pp. 3715 ◽  
Author(s):  
Xinjian Chen ◽  
Di Zeng ◽  
Ying Xu ◽  
Xiaojun Fan
Keyword(s):  
Sustainable Development ◽  
Organic Fertilizer ◽  
Risk Attitude ◽  
Household Survey ◽  
Ordinary Least Squares ◽  
Chemical Fertilizer ◽  
Organic Fertilizers ◽  
Fertilizer Use ◽  
Explanatory Variables ◽  
Hurdle Models

Overuse of chemical fertilizer has led to severe land degradation and environmental pollution in China. Switching to organic fertilizer may improve soil quality and reduce pollution, which is meaningful to the sustainable development of Chinese agriculture. This study examines how farmers’ perceptions and risk preference affect their organic fertilizer investment using a representative rural household survey from Guangxi, a major agricultural region in China. Tobit and double-hurdle models are used to empirically test their impacts on organic fertilizer adoption and investment. An ordinary least squares model is used to regress chemical fertilizer use on the same set of explanatory variables to compare and contrast farmers’ different fertilizer investment behaviors. It is found that both organic fertilizer perceptions and risk attitude significantly affect organic fertilizer investment. Perceived yield-increasing and quality-improving effects encourage organic fertilizer investment, while perceived cost increases discourage it. Moreover, risk-averse farmers are more likely to invest in organic fertilizers. Most of the perceptions affecting organic fertilizer investment have an opposite impact on chemical fertilizer investment, which suggests substitutability between organic and chemical fertilizer. Interventions that aim to improve farmers’ perceptions of organic fertilizer and illustrate its risk-reduction effect could be effective in promoting organic fertilizer use, which can help achieve China’s sustainable development of agriculture.

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