P-splines and GAMLSS: a powerful combination, with an application to zero-adjusted distributions

Stasinopoulos, Dimitrios M.; Rigby, Robert A.; Heller, Gillian Z.; De Bastiani, Fernanda

doi:10.1177/1471082x231176635

Statistical Modelling

2023

DOI: 10.1177/1471082x231176635

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P-splines and GAMLSS: a powerful combination, with an application to zero-adjusted distributions

Dimitrios M. Stasinopoulos,

Robert A. Rigby,

Gillian Z. Heller

et al.

Abstract: P-splines are a versatile statistical modelling tool, dealing with nonlinear relationships between the response and explanatory variable(s). GAMLSS is a distributional regression framework which allows modelling of a response variable using any parametric distribution. The combination of the two methodologies provides one of the most powerful tools in modern regression analysis. This article discusses the application of the two techniques when the response variable is zero-adjusted (or semi-continuous), which … Show more

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2024

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Cited by 3 publications

References 31 publications

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Understanding seasonal cycle of daily extreme temperatures based on generalized additive model for location, scale and shape with smoothing spline

Tu,

Yan,

Qian

2024

Intl Journal of Climatology

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The seasonal cycle (SC) of surface air temperature is a substantial element in climatology. Some basic and important topics in climate change studies are based on accurate and reliable estimation of SCs, such as percentile‐based indices of extremes and probability density function (PDF) changes of daily temperatures. For both of them, most studies characterized SCs using averages of multi‐days windows, which is not smooth and accurate enough representing the extreme thresholds and climatological normal of SCs. It is necessary to construct smooth and reasonable SCs for more accurate estimation of temperature changes on extreme thresholds and PDFs. In this study, we propose a flexible method based on generalized additive models for location scale and shape and penalized b‐spline smoothing technique with respective distributions to construct smooth SCs and SC for extreme temperatures (SCETs). The accuracy of the constructed smooth SCETs is good with the estimation biases of percentiles tending to be zero. The constructed smooth SCET also exhibits good stability over time, such that the magnitude changes of temperatures on each calendar day are close to climatic changes of mean temperature when the concerned period shifts. Based on the constructed smooth SCs, climatic changes by seasons and PDFs between two periods, 1961–1990 and 1991–2020, are examined over China. The increase of thresholds for hot extremes in spring during the recent period is prominent, while the increase of thresholds for daytime cold extremes in summer over a part of central to southern China is also notable. The smooth SCs and SCETs based on our flexible statistical modelling framework can characterize daily extreme temperatures reasonably and accurately, and should be expected to have more applications for a better understanding of climate changes related to distributions and seasonal cycles.

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Understanding seasonal cycle of daily extreme temperatures based on generalized additive model for location, scale and shape with smoothing spline

Tu,

Yan,

Qian

2024

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products

Papacharalampous,

Tyralis,

Doulamis

et al. 2024

Machine Learning with Applications

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Maize productivity based on a distributional regression approach

Ossifo,

Nakamura,

Pedro

et al. 2024

Pesq. agropec. bras.

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The objective of this work was to propose the use of traditional models based on distributional regression models to analyze maize productivity. The experiment was carried out in an alpha lattice design, with three replicates and 24 blocks. Data used refer to 102 maize plants from the permanent collection of the Centro de Desenvolvimento Científico e Tecnológico para a Agricultura of the Universidade Federal de Lavras. For the maize productivity evaluation, the following explanatory variables were used: weight of 100 seed, plant height, ear height, and days to maturation. The initial analyses involved the fitting of four distributions (gamma, generalized gamma, inverse Gaussian, and generalized inverse Gaussian) to the data, in which the gamma distribution showed the best fit based on the Akaike and Bayesian information criteria (AIC and BIC). Cob height has a considerable influence on the productivity variability because as cob height increases, the productivity variability decreases, whereas the covariates weight of 100 seed and days to maturity explain the increasing average of the productivity. The residual analysis shows that the model based on gamma distribution is suitable for explaining the data and providing useful insights for agricultural research and practice.

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P-splines and GAMLSS: a powerful combination, with an application to zero-adjusted distributions

Cited by 3 publications

References 31 publications

Understanding seasonal cycle of daily extreme temperatures based on generalized additive model for location, scale and shape with smoothing spline

Understanding seasonal cycle of daily extreme temperatures based on generalized additive model for location, scale and shape with smoothing spline

Combinations of distributional regression algorithms with application in uncertainty estimation of corrected satellite precipitation products

Maize productivity based on a distributional regression approach

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