On beta regression residuals

Espinheira, Patrícia L.; Ferrari, Silvia L. P.; Cribari‐Neto, Francisco

doi:10.1080/02664760701834931

Cited by 150 publications

(139 citation statements)

References 4 publications

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“…For beta regressions, the adequacy of the assumptions for the random and systematic component can be checked through the behaviour of the residuals, termed 'standardised weighted residuals 2' (Espinheira et al 2008). For example, under the model assumptions, these residuals should be between -3 and 3 with high probability, the scatterplots of the residuals against the observation index number or against the linear predictor should reveal a random scatter around the zero line.…”

Section: Model Checking Toolsmentioning

confidence: 99%

Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines

et al. 2015

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Australia has a low to moderate seismicity by world standards. However, the seismic risk is significant due to the legacy of older buildings constructed prior to the national implementation of an earthquake building standard in Australia. The 1989 Newcastle and the 2010 Kalgoorlie earthquakes are the most recent Australian earthquakes to cause significant damage to unreinforced masonry (URM) and light timber frame structures and have provided the best opportunities to examine the earthquake vulnerability of these building types. This paper describes the two above-mentioned building types with a differentiation of older legacy buildings constructed prior to 1945 to the relatively newer ones constructed after 1945. Furthermore, the paper presents method to utilise the large damage and loss-related data (14,000 insurance claims in Newcastle and 400 surveyed buildings in Kalgoorlie) collected from these events to develop empirical vulnerability functions. The method adopted here followed the GEM empirical vulnerability assessment guidelines which involve preparing a loss database, selecting an appropriate intensity measure, selecting and applying a suitable statistical approach to develop vulnerability functions and the identification of optimum functions. The adopted method uses a rigorous statistical approach to quantify uncertainty in vulnerability functions and provides an optimum solution based on goodness-of-fit tests. The analysis shows that the URM structures built before 1945 are the most vulnerable to earthquake with post-1945 URM structures being the next most vulnerable. Timber structures appear to be the least vulnerable, with little difference observed in the vulnerability of timber buildings built before or after 1945. Moreover, the older structures (both URM and timber) exhibit more scatter in results reflecting greater variation in building vulnerability and performance during

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Section: Model Checking Toolsmentioning

confidence: 99%

Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines

et al. 2015

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“…For the our choice of beta regression instead of normal regression, we also believe that a comprehensive simulation study must be done in order to verify the second choice, as well as the residual analysis for understanding that unexplained situation of the observed proportions close to one (see e.g. Espinheira et al [4]). …”

Section: Discussionmentioning

confidence: 99%

A Bayesian Modelling of Wildfires in Portugal

Silva

Soares

Marques

et al. 2015

CIM Series in Mathematical Sciences

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In the last decade wildfires became a serious problem in Portugal due to socieconomic and climate change trends. In order to analyse wildfire data, we employ beta regression for modelling the proportion of burned wild area, under a Bayesian perspective. Our main goal is to find out fire risk factors that influence the proportion of area burned and what may make a wild area susceptible or resistant to fire. Then, we analyse wildfire data in Portugal during 1990-1994 through Bayesian normal and beta regression models that use Markov chain Monte Carlo methods for estimating quantities of interest.

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“…Doğrusal regresyon modeli uygulamalarda en sık kullanılan regresyon modellerinden biridir. Ancak bağımlı değişkenin değerleri (0,1) aralığında olduğunda ya da bağımlı değişken değerleri oran ya da yüzde ile ifade edildiğinde bu model kullanılamaz (Espinheira, Ferrari ve Cribari-Neto, 2008). Bu durumda doğrusal regresyon yöntemine alternatif olan beta regresyon modeli kullanılır (Ferrari ve Cribari-Neto, 2004 Beta regresyonunda, regresyon modelini oluşturmak için konum parametresi µ ve yayılım parametresi φ için iki bağ fonksiyonu kullanılır.…”

Section: Beta Regresyon Analiziunclassified

Efficiency of State Universities in Turkey During the 2014–2015 Academic Year and Determination of Factors Affecting Efficiency

Türkan

Özel

2017

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On beta regression residuals

Cited by 150 publications

References 4 publications

Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines

Seismic vulnerability functions for Australian buildings by using GEM empirical vulnerability assessment guidelines

A Bayesian Modelling of Wildfires in Portugal

Efficiency of State Universities in Turkey During the 2014–2015 Academic Year and Determination of Factors Affecting Efficiency

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