Relevant bibliographies by topics / GAMLSS (generalized additive models for location, scale and shape) statistics

Academic literature on the topic 'GAMLSS (generalized additive models for location, scale and shape) statistics'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "GAMLSS (generalized additive models for location, scale and shape) statistics"

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Stasinopoulos, Mikis D., Robert A. Rigby, and Fernanda De Bastiani. "GAMLSS: A distributional regression approach." Statistical Modelling 18, no. 3-4 (March 29, 2018): 248–73. http://dx.doi.org/10.1177/1471082x18759144.

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Abstract: A tutorial of the generalized additive models for location, scale and shape (GAMLSS) is given here using two examples. GAMLSS is a general framework for performing regression analysis where not only the location (e.g., the mean) of the distribution but also the scale and shape of the distribution can be modelled by explanatory variables.
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Mayr, Andreas, Matthias Schmid, Annette Pfahlberg, Wolfgang Uter, and Olaf Gefeller. "A permutation test to analyse systematic bias and random measurement errors of medical devices via boosting location and scale models." Statistical Methods in Medical Research 26, no. 3 (April 24, 2015): 1443–60. http://dx.doi.org/10.1177/0962280215581855.

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Measurement errors of medico-technical devices can be separated into systematic bias and random error. We propose a new method to address both simultaneously via generalized additive models for location, scale and shape (GAMLSS) in combination with permutation tests. More precisely, we extend a recently proposed boosting algorithm for GAMLSS to provide a test procedure to analyse potential device effects on the measurements. We carried out a large-scale simulation study to provide empirical evidence that our method is able to identify possible sources of systematic bias as well as random error under different conditions. Finally, we apply our approach to compare measurements of skin pigmentation from two different devices in an epidemiological study.
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Xiong, Lihua, Cong Jiang, and Tao Du. "Statistical attribution analysis of the nonstationarity of the annual runoff series of the Weihe River." Water Science and Technology 70, no. 5 (July 21, 2014): 939–46. http://dx.doi.org/10.2166/wst.2014.322.

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Time-varying moments models based on Pearson Type III and normal distributions respectively are built under the generalized additive model in location, scale and shape (GAMLSS) framework to analyze the nonstationarity of the annual runoff series of the Weihe River, the largest tributary of the Yellow River. The detection of nonstationarities in hydrological time series (annual runoff, precipitation and temperature) from 1960 to 2009 is carried out using a GAMLSS model, and then the covariate analysis for the annual runoff series is implemented with GAMLSS. Finally, the attribution of each covariate to the nonstationarity of annual runoff is analyzed quantitatively. The results demonstrate that (1) obvious change-points exist in all three hydrological series, (2) precipitation, temperature and irrigated area are all significant covariates of the annual runoff series, and (3) temperature increase plays the main role in leading to the reduction of the annual runoff series in the study basin, followed by the decrease of precipitation and the increase of irrigated area.
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Greven, Sonja, and Fabian Scheipl. "A general framework for functional regression modelling." Statistical Modelling 17, no. 1-2 (February 2017): 1–35. http://dx.doi.org/10.1177/1471082x16681317.

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Researchers are increasingly interested in regression models for functional data. This article discusses a comprehensive framework for additive (mixed) models for functional responses and/or functional covariates based on the guiding principle of reframing functional regression in terms of corresponding models for scalar data, allowing the adaptation of a large body of existing methods for these novel tasks. The framework encompasses many existing as well as new models. It includes regression for ‘generalized’ functional data, mean regression, quantile regression as well as generalized additive models for location, shape and scale (GAMLSS) for functional data. It admits many flexible linear, smooth or interaction terms of scalar and functional covariates as well as (functional) random effects and allows flexible choices of bases—particularly splines and functional principal components—and corresponding penalties for each term. It covers functional data observed on common (dense) or curve-specific (sparse) grids. Penalized-likelihood-based and gradient-boosting-based inference for these models are implemented in R packages refund and FDboost , respectively. We also discuss identifiability and computational complexity for the functional regression models covered. A running example on a longitudinal multiple sclerosis imaging study serves to illustrate the flexibility and utility of the proposed model class. Reproducible code for this case study is made available online.
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Berger, Moritz, and Matthias Schmid. "Flexible modeling of ratio outcomes in clinical and epidemiological research." Statistical Methods in Medical Research 29, no. 8 (December 9, 2019): 2250–68. http://dx.doi.org/10.1177/0962280219891195.

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In medical studies one frequently encounters ratio outcomes. For modeling these right-skewed positive variables, two approaches are in common use. The first one assumes that the outcome follows a normal distribution after transformation (e.g. a log-normal distribution), and the second one assumes gamma distributed outcome values. Classical regression approaches relate the mean ratio to a set of explanatory variables and treat the other parameters of the underlying distribution as nuisance parameters. Here, more flexible extensions for modeling ratio outcomes are proposed that allow to relate all the distribution parameters to explanatory variables. The models are embedded into the framework of generalized additive models for location, scale and shape (GAMLSS), and can be fitted using a component-wise gradient boosting algorithm. The added value of the new modeling approach is demonstrated by the analysis of the LDL/HDL cholesterol ratio, which is a strong predictor of cardiovascular events, using data from the German Chronic Kidney Disease Study. Particularly, our results confirm various important findings on risk factors for cardiovascular events.
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Oliveira, Tiago Almeida, Sílvio Fernando Alves Xavier Júnior, Gláucia Amorim Faria, Beatriz Garcia Lopes, Ednário Mendonça Barbosa, and Ana Patrícia Bastos Peixoto. "An Application of Generalized Additive Models of Location, Scale, and Shape (GAMLSS) to estimate the Eucalyptus Height." Ciência e Natura 42 (May 13, 2020): e15. http://dx.doi.org/10.5902/2179460x41710.

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The Generalized Additive Models for Location, Scale, and Shape (GAMLSS) are a recent class of models that further flexibilitythe distribution of the response variable. The regression analysis has been used to model biological phenomena, and its variousmodalities have met the need for its use with precision. However, there are situations in which the adjustment of models with moreflexible assumptions in the specification of the distribution of the response variable becomes indispensable, thus justifying the useof GAMLSS. The study of plant growth curves has full application in agricultural research; thus, it is crucial to know the habits ofgrowth and development of forest species is crucial for reforestation programs and in the most diverse researches. The study aimedto model the growth of Eucalyptus through the adjusting of Generalized Additive Models for Location, Scale, and Shape, in orderto promote improvements on crop productivity. Considering all parameters of the independent variable (time) under GAMLSSclass modeling, the distribution model ST3 presented better results.
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Roquim, Fernanda V., Thiago G. Ramires, Luiz R. Nakamura, Ana J. Righetto, Renato R. Lima, and Rayne A. Gomes. "Building flexible regression models: including the Birnbaum-Saunders distribution in the gamlss package." Semina: Ciências Exatas e Tecnológicas 42, no. 2 (November 3, 2021): 163. http://dx.doi.org/10.5433/1679-0375.2021v42n2p163.

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Generalized additive models for location, scale and shape (GAMLSS) are a very flexible statistical modeling framework, being an important generalization of the well-known generalized linear models and generalized additive models. Their main advantage is that any probability distribution (that does not necessarily belong to the exponential family) can be considered to model the response variable and different regression structures can be fitted in each of its parameters. Currently, there are more than 100 distributions that are already implemented in the gamlss package in R software. Nevertheless, researchers can implement different distributions if they are not yet available, e.g., the Birnbaum-Saunders (BS) distribution, which is widely used in fatigue studies. In this paper we make available all codes regarding the inclusion of the BS distribution in the gamlss package, and then present a simple application related to air quality data for illustration purposes
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Ramires, Thiago G., Luiz R. Nakamura, Ana J. Righetto, Renan J. Carvalho, Lucas A. Vieira, and Carlos A. B. Pereira. "Comparison between Highly Complex Location Models and GAMLSS." Entropy 23, no. 4 (April 16, 2021): 469. http://dx.doi.org/10.3390/e23040469.

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This paper presents a discussion regarding regression models, especially those belonging to the location class. Our main motivation is that, with simple distributions having simple interpretations, in some cases, one gets better results than the ones obtained with overly complex distributions. For instance, with the reverse Gumbel (RG) distribution, it is possible to explain response variables by making use of the generalized additive models for location, scale, and shape (GAMLSS) framework, which allows the fitting of several parameters (characteristics) of the probabilistic distributions, like mean, mode, variance, and others. Three real data applications are used to compare several location models against the RG under the GAMLSS framework. The intention is to show that the use of a simple distribution (e.g., RG) based on a more sophisticated regression structure may be preferable than using a more complex location model.
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Sá, Ana C. L., Maria A. A. Turkman, and José M. C. Pereira. "Exploring fire incidence in Portugal using generalized additive models for location, scale and shape (GAMLSS)." Modeling Earth Systems and Environment 4, no. 1 (January 16, 2018): 199–220. http://dx.doi.org/10.1007/s40808-017-0409-6.

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Barajas, Freddy Hernández, Mabel Torres, Lina Arteaga, and Cristina Castro. "GAMLSS models applied in the treatment of agro-industrial waste." Comunicaciones en Estadística 8, no. 2 (December 30, 2015): 245. http://dx.doi.org/10.15332/s2027-3355.2015.0002.07.

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In this paper, we present an application of GAMLSS (Generalized Additive Models for Location, Shape and Scale) to study bacterial cellulose production from agro-industrial waste. An experiment was conducted to research the effects of pH and cultivation time on bacterial cellulose yield obtained from discarded bananas. Several models were fitted to the collected data to determine an estimated expression for the mean and variance of bacterial cellulose yield. We found that the mean and variance of cellulose yield decrease as pH increases, while the opposite occurs as cultivation time increases.
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Dissertations / Theses on the topic "GAMLSS (generalized additive models for location, scale and shape) statistics"

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MacGillivray, Phyllis Frances. "Tracking phenological shifts and evolutionary impacts related to climate change." Thesis, 2013. http://hdl.handle.net/2440/83736.

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Phenology is the study of recurring life-cycle events that are initiated and driven by environmental factors, such as the response of flowering time to the prevailing climate. Ongoing climate change is thus expected to impact on the flowering time of plant populations with consequences for reproductive success in the short term and their survival in the long term, along with potentially widespread repercussions for associated ecological health and function. Tracking phenological shifts in response to past climate variability provides a benchmark or reference point for gauging future impacts. The introductory chapter of this thesis presents a review of the literature as it relates to my research documented in the following three chapters. Chapter 2 provides an exploration of the impacts of climate on the flowering phenology of the South Australian endemic Diuris orchid genus. A statistical analysis, trialling the suitability of Generalized Additive Models for Location, Scale and Shape (GAMLSS) for modelling of a long-term, historical dataset showed a significant curvilinear trend, with peak flowering advancing over time. This investigation was extended to determine the main and interactive effects of temperature and rainfall as specific drivers of Diuris flowering phenology (Chapter 3). A highly significant flowering response to seasonal temperatures and rainfall was identified, with shifts to earlier flowering in warmer and drier seasons expected under climate change scenarios. Chapter 4 comprises various analyses of a 44-year replicate data set of 112 Pyrus (pear) trees growing at the University of Adelaide Waite Arboretum. This aspect of my research provided a unique opportunity to study the phenological responses of a non-native genus at the species and individual levels, when subjected to identical environmental conditions. A general response to minimum temperature was, on occasions, overridden by an early flowering response initiated by drought-breaking rains. This study also allowed a comparison to be made between Pyrus phenology in the northern and southern hemispheres, and an insight into the potential economic impacts for South Australian horticulture. Evolutionary implications for all study species arising from climatically-induced phenological shifts are outlined in Chapter 5, including a consideration of the likelihood that the rate of evolutionary change will be sufficient to keep pace with predicted climate change scenarios. Findings from these investigations are then considered in relation to the selection of bioclimatic indicators. In this sixth chapter, I challenge the validity of many assertions and assumptions presented in the literature. This thesis concludes that the stresses of ongoing climate change will have a selective impact on the reproductive fitness of flowering plants growing in South Australia. Outcomes will vary dependent upon individual populations and species, geographic location and evolutionary history.
Thesis (Ph.D.) -- University of Adelaide, School of Earth and Environmental Sciences, 2013
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Conference papers on the topic "GAMLSS (generalized additive models for location, scale and shape) statistics"

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Shimizu, K., M. Nakasone, T. Kubota, N. Miyata, S. Katoh, K. Kitano, J. Nakajima, Y. Yatomi, and D. Takai. "Japanese Non-Smokers' Pledicted DLco Formula Using Generalized Additive Models for Location, Scale and Shape(GAMLSS)." In American Thoracic Society 2019 International Conference, May 17-22, 2019 - Dallas, TX. American Thoracic Society, 2019. http://dx.doi.org/10.1164/ajrccm-conference.2019.199.1_meetingabstracts.a5450.

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