Generalized Additive Models for Location, Scale and Shape

Rigby, Robert A.; Stasinopoulos, Dimitrios

doi:10.1111/j.1467-9876.2005.00510.x

Cited by 2,348 publications

(2,379 citation statements)

References 92 publications

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“…We used the FLEXMIX [17] and GAMLSS.MX [18] packages in the R statistical software version 2.5.1 [19] to fit continuous finite mixture models based on a latent-class regression approach. Both packages employ an expectation-maximization (EM) algorithm that uses maximum likelihood to estimate the model parameters and calculates the probability of belonging to each component for every observation.…”

Section: Methodsmentioning

confidence: 99%

Analysis of rubella antibody distribution from newborn dried blood spots using finite mixture models

Hardelid¹,

Williams²,

Dezateux³

et al. 2008

Epidemiol. Infect.

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SUMMARYEluted dried blood spot specimens from newborn screening, collected in 2004 in North Thames and anonymously linked to birth registration data, were tested for maternally acquired rubella IgG antibody as a proxy for maternal antibody concentration using an enzyme-linked immunosorbent assay. Finite mixture regression models were fitted to the antibody concentrations from 1964 specimens. The Bayesian Information Criterion (BIC) was used as a model selection criterion to avoid over-fitting the number of mixture model components. This allowed investigation of the independent effect of maternal age and maternal country of birth on rubella antibody concentration without dichotomizing the outcome variable using cut-off values set a priori. Mixture models are a highly useful method of analysis in seroprevalence studies of vaccine-preventable infections in which preset cut-off values may overestimate the size of the seronegative population.

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Section: Methodsmentioning

confidence: 99%

Analysis of rubella antibody distribution from newborn dried blood spots using finite mixture models

Hardelid¹,

Williams²,

Dezateux³

et al. 2008

Epidemiol. Infect.

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“…. , η θ K ) like in distributional regression (Rigby and Stasinopoulos, 2005), or settings where in addition to the main model η some nuisance parameter (e.g., a scale parameter φ for negative binomial regression) should be optimized simultaneously. Boosting the latter model can be achieved by first carrying out the classical gradient-fitting and updating steps for the additive predictor (see Algorithm 1) and second by updating the nuisance parameter φ, both in each iteration step.…”

Section: Boosting For Multiple Dimensionsmentioning

confidence: 99%

Boosting joint models for longitudinal and time‐to‐event data

et al. 2017

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Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in clinical studies where longitudinal outcomes are recorded alongside event times. Those two processes are often linked and the two outcomes should thus be modeled jointly in order to prevent the potential bias introduced by independent modelling. Commonly, joint models are estimated in likelihood based expectation maximization or Bayesian approaches using frameworks where variable selection is problematic and which do not immediately work for high-dimensional data. In this paper, we propose a boosting algorithm tackling these challenges by being able to simultaneously estimate predictors for joint models and automatically select the most influential variables even in high-dimensional data situations. We analyse the performance of the new algorithm in a simulation study and apply it to the Danish cystic fibrosis registry which collects longitudinal lung function data on patients with cystic fibrosis together with data regarding the onset of pulmonary infections. This is the first approach to combine state-of-the art algorithms from the field of machine-learning with the model class of joint models, providing a fully data-driven mechanism to select variables and predictor effects in a unified framework of boosting joint models.

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“…"100-year flood") using the North Atlantic Oscillation index and a reservoir index as external covariates (Machado et al, 2015). This non-stationary modelling was based on Generalized Additive Models for Location, Scale and Shape parameters (GAMLSS; Rigby and Stasinopoulos, 2005) that described the temporal variation of statistical parameters (mean, variance) in probability distribution functions (Villarini et al, 2010;López and Francés, 2013). In this example, the non-stationary models show that the peak flood associated with a "100-year" flood (0.01 annual exceedance probability) may range between 4180 and 560 m 3 s −1 , whereas the same model under stationary conditions provided the best fitting results to a log-normal distribution, with a discharge of 1450 m 3 s −1 (Fig.…”

Section: Historical Floods In a Non-stationary Hydrologymentioning

confidence: 99%

Quantitative historical hydrology in Europe

Benito

Brázdil

Herget

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. In recent decades, the quantification of flood hydrological characteristics (peak discharge, hydrograph shape, and runoff volume) from documentary evidence has gained scientific recognition as a method to lengthen flood records of rare and extreme events. This paper describes the methodological evolution of quantitative historical hydrology under the influence of developments in hydraulics and statistics. In the 19th century, discharge calculations based on flood marks were the only source of hydrological data for engineering design, but were later left aside in favour of systematic gauge records and conventional hydrological procedures. In the last two decades, there has been growing scientific and public interest in understanding long-term patterns of rare floods, in maintaining the flood heritage and memory of extremes, and developing methods for deterministic and statistical application to different scientific and engineering problems. A compilation of 46 case studies across Europe with reconstructed discharges demonstrates that (1) in most cases present flood magnitudes are not unusual within the context of the last millennium, although recent floods may exceed past floods in some temperate European rivers (e.g. the Vltava and Po rivers); (2) the frequency of extreme floods has decreased since the 1950s, although some rivers (e.g. the Gardon and Ouse rivers) show a reactivation of rare events over the last two decades. There is a great potential for gaining understanding of individual extreme events based on a combined multiproxy approach (palaeoflood and documentary records) providing high-resolution time flood series and their environmental and climatic changes; and for developing non-systematic and non-stationary statistical models based on relations of past floods with external and internal covariates under natural low-frequency climate variability.

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Generalized Additive Models for Location, Scale and Shape

Cited by 2,348 publications

References 92 publications

Analysis of rubella antibody distribution from newborn dried blood spots using finite mixture models

Analysis of rubella antibody distribution from newborn dried blood spots using finite mixture models

Boosting joint models for longitudinal and time‐to‐event data

Quantitative historical hydrology in Europe

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