The development of fire-induced damage functions for forest recreation activity in Alberta, Canada

2010 ◽  
Vol 19 (1) ◽  
pp. 63 ◽  
Author(s):  
Michael Rausch ◽  
Peter C. Boxall ◽  
Arunas P. Verbyla
Keyword(s):  
Negative Binomial ◽  
Stated Preference ◽  
Damage Function ◽  
Fire Damage ◽  
Canadian Rocky Mountains ◽  
Count Data Models ◽  
Damage Functions ◽  
Forest Recreation ◽  
Stated Preference Data ◽  
Camping Trip

This study develops an intertemporal fire damage function for forest recreation activity in the eastern slopes region of the Canadian Rocky Mountains. The methodology employed combined revealed-stated preference data in which the behavioral response variable was annual camping trip frequencies. Photographs were used to portray changes in stand ages and related changes in trip frequencies. The data were analysed using negative binomial count data models. Unlike previous studies employing similar methods, a random effects specification was used to develop trip demand parameters. The results suggest that fires initially decrease annual trips from ~2.56 to 1.0 after the burn. As the stand ages, the effect of the fire decreases until ~12 years after the fire when the trip frequencies recover to about their previous ‘old-growth’ levels. This function is different from others described in the literature for similar mountain ecosystems in North America.

scholarly journals A Joint Estimation Method to Combine Dichotomous Choice CVM Models with Count Data TCM Models Corrected for Truncation and Endogenous Stratification

2008 ◽  
Vol 40 (2) ◽  
pp. 681-695 ◽  
Author(s):  
Juan Marcos González ◽  
John B. Loomis ◽  
Armando González-Cabán
Keyword(s):  
Count Data ◽  
Stated Preference ◽  
Estimation Method ◽  
Joint Estimation ◽  
Site Quality ◽  
Count Data Models ◽  
Preference Data ◽  
Estimation Efficiency ◽  
Stated Preference Data ◽  
Endogenous Stratification

We update the joint estimation of revealed and stated preference data of previously published research to allow for joint estimation of the Travel Cost Method (TCM) portion using count data models. The TCM estimation also corrects for truncation and endogenous stratification as well as overdispersion. The joint estimation allows for testing consistency of behavior between revealed and stated preference data rather than imposing it. We find little gain in estimation efficiency, but our joint estimation might make a significant improvement in estimation efficiency when the contingent valuation scenarios involve major changes in site quality not reflected in the TCM data.

scholarly journals Beta-binomial models for meta-analysis with binary outcomes: Variations, extensions, and additional insights from econometrics

2021 ◽  
pp. 263208432199622
Author(s):  
Tim Mathes ◽  
Oliver Kuss
Keyword(s):  
Simulation Study ◽  
Count Data ◽  
Negative Binomial ◽  
Meta Analysis ◽  
Negative Binomial Regression ◽  
Binary Outcomes ◽  
Small Scale ◽  
Panel Count Data ◽  
Count Data Models ◽  
Meta Analyses

Background Meta-analysis of systematically reviewed studies on interventions is the cornerstone of evidence based medicine. In the following, we will introduce the common-beta beta-binomial (BB) model for meta-analysis with binary outcomes and elucidate its equivalence to panel count data models. Methods We present a variation of the standard “common-rho” BB (BBST model) for meta-analysis, namely a “common-beta” BB model. This model has an interesting connection to fixed-effect negative binomial regression models (FE-NegBin) for panel count data. Using this equivalence, it is possible to estimate an extension of the FE-NegBin with an additional multiplicative overdispersion term (RE-NegBin), while preserving a closed form likelihood. An advantage due to the connection to econometric models is, that the models can be easily implemented because “standard” statistical software for panel count data can be used. We illustrate the methods with two real-world example datasets. Furthermore, we show the results of a small-scale simulation study that compares the new models to the BBST. The input parameters of the simulation were informed by actually performed meta-analysis. Results In both example data sets, the NegBin, in particular the RE-NegBin showed a smaller effect and had narrower 95%-confidence intervals. In our simulation study, median bias was negligible for all methods, but the upper quartile for median bias suggested that BBST is most affected by positive bias. Regarding coverage probability, BBST and the RE-NegBin model outperformed the FE-NegBin model. Conclusion For meta-analyses with binary outcomes, the considered common-beta BB models may be valuable extensions to the family of BB models.

scholarly journals GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Symmetry ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 362
Author(s):  
Arshad Jamal ◽  
Tahir Mahmood ◽  
Muhamad Riaz ◽  
Hassan M. Al-Ahmadi
Keyword(s):  
Real Time ◽  
Control Charts ◽  
Negative Binomial ◽  
Highway Safety ◽  
Monitoring Methods ◽  
Count Data Models ◽  
Crash Data ◽  
Safety Surveillance ◽  
Study Results ◽  
Best Fit

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

scholarly journals A Model of Car and P&BR Choice in Commuting Trips Combining the Experiment Day Data with the Stated Preference Data

1999 ◽  
Vol 16 ◽  
pp. 955-961
Author(s):  
Hongzhi Guan ◽  
Kazuo Nishii ◽  
Atsushi Tanaka ◽  
Takeshi Morikawa
Keyword(s):  
Stated Preference ◽  
Preference Data ◽  
Stated Preference Data

Economic evaluation of a prospective Farm Animal Welfare program in Turkey

2019 ◽  
Vol 122 (1) ◽  
pp. 345-362
Author(s):  
Özlem Turan ◽  
Serkan Gurluk ◽  
Abdulhakim Madiyoh
Keyword(s):  
Animal Welfare ◽  
Design Methodology ◽  
Stated Preference ◽  
Farm Animal ◽  
Farm Animal Welfare ◽  
Policy Makers ◽  
Payment Card ◽  
Content Type ◽  
Base Level ◽  
Stated Preference Data

Purpose The purpose of this paper is to examine producer preferences for changing Farm Animal Welfare (FAW) levels in regards to sheep and goat husbandry in Bursa-Turkey. Design/methodology/approach The paper tests “panel estimators” in a stated preference data by using the payment card question format. Probit panels are employed to measure individual effects on FAW levels by considering producers’ willingness to accept. Three different FAW levels were identified for valuation as “base” level, “better” level, and the “best” level. The current study suggests a protocol with WTA(P) nomenclature to resolve complexity issues in FAW studies by investigating producers rather than consumers because the scenarios regarding FAW levels include quite technical and difficult topics which are vague to consumers. Findings If half of the total number of the sheep and goats in Turkey are assumed to be in bad animal welfare conditions, which are worse than base level, the non-use benefits of bringing them to at least the base level would be about US$130.3m. Figures would be 166.2m US$/year and 175m US$/year for “better” and “best” FAW conditions, respectively. Originality/value This paper provides a contribution to the existing literature by examining the producers’ responses to new FAW schemes. Also it helps policy makers to understand producers’ environmental behavior as well as their sensitivity to FAW schemes.

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