Relevant bibliographies by topics / Flexible regression models

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Flexible regression models'

Author: Grafiati
Published: 9 March 2023
Last updated: 19 July 2025

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Journal articles on the topic "Flexible regression models"

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Gurmu, Shiferaw, and John Elder. "Flexible Bivariate Count Data Regression Models." Journal of Business & Economic Statistics 30, no. 2 (2012): 265–74. http://dx.doi.org/10.1080/07350015.2011.638816.

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O'Donnell, David, Alastair Rushworth, Adrian W. Bowman, E. Marian Scott, and Mark Hallard. "Flexible regression models over river networks." Journal of the Royal Statistical Society: Series C (Applied Statistics) 63, no. 1 (2013): 47–63. http://dx.doi.org/10.1111/rssc.12024.

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Nikulin, M., and Hong-Dar Isaac Wu. "Flexible regression models for carcinogenesis studies." Journal of Mathematical Sciences 145, no. 2 (2007): 4880–93. http://dx.doi.org/10.1007/s10958-007-0322-z.

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Lee, Young K., Enno Mammen, and Byeong U. Park. "Flexible generalized varying coefficient regression models." Annals of Statistics 40, no. 3 (2012): 1906–33. http://dx.doi.org/10.1214/12-aos1026.

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Durrleman, Sylvain, and Richard Simon. "Flexible regression models with cubic splines." Statistics in Medicine 8, no. 5 (1989): 551–61. http://dx.doi.org/10.1002/sim.4780080504.

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Bonat, Wagner Hugo, and Célestin C. Kokonendji. "Flexible Tweedie regression models for continuous data." Journal of Statistical Computation and Simulation 87, no. 11 (2017): 2138–52. http://dx.doi.org/10.1080/00949655.2017.1318876.

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Dahl, Christian M., and Svend Hylleberg. "Flexible regression models and relative forecast performance." International Journal of Forecasting 20, no. 2 (2004): 201–17. http://dx.doi.org/10.1016/j.ijforecast.2003.09.002.

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Santías, Francisco Reyes, Carmen Cadarso-Suárez, and María Xosé Rodríguez-Álvarez. "Estimating hospital production functions through flexible regression models." Mathematical and Computer Modelling 54, no. 7-8 (2011): 1760–64. http://dx.doi.org/10.1016/j.mcm.2010.11.087.

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da Silva, Nívea B., Marcos O. Prates, and Flávio B. Gonçalves. "Bayesian linear regression models with flexible error distributions." Journal of Statistical Computation and Simulation 90, no. 14 (2020): 2571–91. http://dx.doi.org/10.1080/00949655.2020.1783261.

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Shaw, J. E. H. "Numerical Bayesian Analysis of Some Flexible Regression Models." Statistician 36, no. 2/3 (1987): 147. http://dx.doi.org/10.2307/2348507.

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Dissertations / Theses on the topic "Flexible regression models"

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Mukherjee, Kathakali Ghosh. "Flexible regression models for functional neuroimaging." Thesis, University of Glasgow, 2016. http://theses.gla.ac.uk/7286/.

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Current practice for analysing functional neuroimaging data is to average the brain signals recorded at multiple sensors or channels on the scalp over time across hundreds of trials or replicates to eliminate noise and enhance the underlying signal of interest. These studies recording brain signals non-invasively using functional neuroimaging techniques such as electroencephalography (EEG) and magnetoencephalography (MEG) generate complex, high dimensional and noisy data for many subjects at a number of replicates. Single replicate (or single trial) analysis of neuroimaging data have gained focus as they are advantageous to study the features of the signals at each replicate without averaging out important features in the data that the current methods employ. The research here is conducted to systematically develop flexible regression mixed models for single trial analysis of specific brain activities using examples from EEG and MEG to illustrate the models. This thesis follows three specific themes: i) artefact correction to estimate the `brain' signal which is of interest, ii) characterisation of the signals to reduce their dimensions, and iii) model fitting for single trials after accounting for variations between subjects and within subjects (between replicates). The models are developed to establish evidence of two specific neurological phenomena - entrainment of brain signals to an α band of frequencies (8-12Hz) and dipolar brain activation in the same α frequency band in an EEG experiment and a MEG study, respectively.
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Roemmele, Eric S. "A Flexible Zero-Inflated Poisson Regression Model." UKnowledge, 2019. https://uknowledge.uky.edu/statistics_etds/38.

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A practical problem often encountered with observed count data is the presence of excess zeros. Zero-inflation in count data can easily be handled by zero-inflated models, which is a two-component mixture of a point mass at zero and a discrete distribution for the count data. In the presence of predictors, zero-inflated Poisson (ZIP) regression models are, perhaps, the most commonly used. However, the fully parametric ZIP regression model could sometimes be restrictive, especially with respect to the mixing proportions. Taking inspiration from some of the recent literature on semiparametric mixtures of regressions models for flexible mixture modeling, we propose a semiparametric ZIP regression model. We present an "EM-like" algorithm for estimation and a summary of asymptotic properties of the estimators. The proposed semiparametric models are then applied to a data set involving clandestine methamphetamine laboratories and Alzheimer's disease.
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Lynch, James Charles. "A flexible class of models for regression modelling of multivariate failure time data /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/9561.

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Fischer, Manfred M. "Neural networks. A class of flexible non-linear models for regression and classification." Elgar, 2015. http://epub.wu.ac.at/4763/1/NN%2DHandbook%2Dchapter_Fischer_20120809.pdf.

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BERNASCONI, DAVIDE PAOLO. "Dynamic prediction in survival analysis with binary non-reversible time-dependent treatment indicator." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2015. http://hdl.handle.net/10281/76772.

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Negli studi clinici spesso è di interesse confrontare la sopravvivenza di pazienti appartenenti a due o più gruppi di trattamento. In alcune situazioni, la classificazione non è effettuata all’inizio del follow-up ma cambia nel tempo. Ad esempio, tutti i pazienti sono sottoposti ad un trattamento iniziale ed alcuni lo continuano mentre altri cambiano dopo un certo periodo di tempo. In questo caso il trattamento è rappresentato da una variabile binaria tempo-dipendente. Un contesto tipico è il confronto tra chemioterapia e trapianto di cellule staminali nella Leucemia Linfoblastica Acuta. In questa situazione, il metodo Kaplan-Meier non è utilizzabile in quanto affetto da immortal time bias. Due approcci non-parametrici alternativi sono stati proposti in letteratura. Andersen et al. (1983) suggeriscono di classificare i pazienti ad un tempo “landmark” che corrisponde al punto iniziale della stima della curva di sopravvivenza, includendo solo i pazienti ancora a rischio al landmark. Il secondo metodo, proposto da Simon e Makuch (1984), consiste nell’aggiornamento dinamico dei “risk sets” dei due gruppi di trattamento tempo-dipendenti. Entrambi i metodi sono stati presentati in maniera euristica e senza specificare le quantità teoriche che corrispondono agli stimatori proposti. Perciò, l’interpretazione delle curve stimate dai due metodi non è mai stata chiarita. Quando l’interesse non è rivolto alla sopravvivenza globale ma alla predizione profilo-specifica, ovvero tenendo conto delle caratteristiche individuali dei soggetti, occorre utilizzare metodi di regressione parametrici o semi-parametrici. Il modello di Cox è quello più popolare ma in presenza di effetti tempo-dipendenti e/o di covariate tempo-dipendenti non può essere utilizzato per ottenere delle curve. Tra le possibili alternative sono stati considerati il modello parametrico di Hanley e Miettinen (2009) e il modello di regressione semi-parametrico basato sul landmark di Van Houwelingen (2007). Il primo è basato sulla stima della funzione azzardo nel tempo applicando una regressione logistica ad un dataset esteso creato dalla suddivisione del tempo di sopravvivenza osservato di ciascun soggetto in un certo numero di unità di tempo e trattando il numero di eventi in ogni singolo intervallo di tempo come una variabile casuale Binomiale. Il secondo metodo scaturisce dall’idea di utilizzare il modello di Cox su molteplici partizioni del dataset ciascuna creata partendo da un tempo landmark progressivo e includendo solo i soggetti a rischio al landmark; la classificazione del trattamento per questi pazienti è fissata a quel tempo consentendo di aggiornare dinamicamente il valore delle covariate tempo-dipendenti in ciascun modello e permettendo ai coefficienti stimati di variare nel tempo. Gli scopi del presente lavoro sono la revisione e lo sviluppo di metodi per: 1) descrivere la sopravvivenza in funzione di un covariata binaria tempo-dipendente sia da una prospettiva fissa sia dinamicamente nel tempo; 2) la valutazione dell’impatto su queste quantità dei fattori prognostici, in particolare il tempo di attesa al trapianto, utilizzando dei parametri interpretabili; 3) lo sviluppo di predizioni profilo-specifiche. Nella prima parte del lavoro si intende chiarire il significato delle le quantità teoriche stimate dai metodi landmark e Simon e Makuch. In aggiunta, si presenta un approccio innovativo basato su domande controfattuali e predizione dinamica, verificando la validità dei risultati attraverso delle simulazioni. Nella seconda parte, si presentano i modelli di regressione di Hanley-Miettinen e del landmark e si mostra come utilizzarli per ottenere la stima dell’effetto del tempo i attesa al trapianto e per produrre delle predizioni profilo-specifiche su dati reali inerenti a pazienti affetti da Leucemia Linfoblastica Acuta, confrontando la performance dei modelli attraverso delle simulazioni.<br>In clinical studies it is often of interest to compare the survival experience of patients in two or more treatment groups. In some situations the categorization is not fixed at baseline but changes during the follow-up, where patients, for example, start from an initial treatment and either continue it or switch to an alternative one after some time (waiting time). Thus, treatment is a binary non reversible time-dependent variable. A typical problem is comparing outcomes of chemotherapy vs stem-cell transplantation in Acute Lymphoblastic Leukemia (ALL) where patients are treated initially with chemotherapy and during the follow-up they can receive bone marrow transplant. In this context, the standard Kaplan-Meier method is unreliable since it is affected by the immortal time bias. Two alternative non-parametric approaches were proposed in the literature. Andersen et al. (1983) suggests to classify patients at a landmark time which corresponds to the starting point for the estimation of the Kaplan-Meier survival curve, involving only patients still at risk at the landmark. The second, proposed by Simon and Makuch (1984), consists in dynamically updating in time the risk set of the two time-dependent treatment groups. Both methods were presented mostly relying on heuristic bases and without specifying the theoretical quantities corresponding to the proposed estimators. Thus, the interpretations of the curves estimated by the two methods was never clarified. When the focus is not on the overall survival experience but rather on profile-specific prediction, i.e. accounting for the individual characteristics of the subjects, one must resort to semi-parametric or parametric regression models. The Cox model is the most popular one but in the presence of time-varying effects and/or time-dependent covariates it cannot be used to obtain survival curves. Among the possible alternatives we considered the full parametric model by Hanley and Miettinen (2009) and the semi-parametric landmark regression model by Van Houwelingen (2007). The first is based on estimating the hazard function over time by applying a logistic regression to an expanded dataset created by splitting the observed survival time of each subject into a number of time-units and to treat the number of events in every single interval as a Binomial random variable. The second originates from the idea of fitting the Cox model to multiple subsets of data, each one created starting from a sliding landmark time point and including only the subjects at risk at the landmark; the treatment classification for these patients is frozen at that time allowing to dynamically update the time-dependent covariates in each model and to let the parameter estimates to vary in time. The aims of the dissertation are reviewing and developing methods for: 1) the description of the survival experience according to a binary time-dependent treatment indicator both from a fixed perspective and dynamically update in time; 2) the assessment of the impact on these quantities of prognostic factors, in particular the waiting time to transplant, through interpretable parameters; 3) the development of profile-specific predictions. In the first part of this work we wish to clarify the theoretical quantities estimated by the landmark and Simon-Makuch methods. In addition, we present a novel approach based on counterfactual questions and dynamic prediction, checking the validity of our findings using simulations. In the second part, we review the Hanley-Miettinen and landmark regression models and we show how to use them to properly estimate the effect of waiting time to transplant and to make profile-specific dynamic predictions on a real dataset on ALL, comparing the performance of the two models using simulations.
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Luo, Zairen. "Flexible Pavement Condition Model Using Clusterwise Regression and Mechanistic-Empirical Procedure for Fatigue Cracking Modeling." See Full Text at OhioLINK ETD Center (Requires Adobe Acrobat Reader for viewing), 2005. http://www.ohiolink.edu/etd/view.cgi?toledo1133560069.

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Dissertation (Ph.D.)--University of Toledo, 2005.<br>Typescript. "A dissertation [submitted] as partial fulfillment of the requirements of the Doctor of Philosophy degree in Engineering." Bibliography: leaves 90-99.
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Hossain, Shahadut. "Dealing with measurement error in covariates with special reference to logistic regression model: a flexible parametric approach." Thesis, University of British Columbia, 2007. http://hdl.handle.net/2429/408.

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In many fields of statistical application the fundamental task is to quantify the association between some explanatory variables or covariates and a response or outcome variable through a suitable regression model. The accuracy of such quantification depends on how precisely we measure the relevant covariates. In many instances, we can not measure some of the covariates accurately, rather we can measure noisy versions of them. In statistical terminology this is known as measurement errors or errors in variables. Regression analyses based on noisy covariate measurements lead to biased and inaccurate inference about the true underlying response-covariate associations. In this thesis we investigate some aspects of measurement error modelling in the case of binary logistic regression models. We suggest a flexible parametric approach for adjusting the measurement error bias while estimating the response-covariate relationship through logistic regression model. We investigate the performance of the proposed flexible parametric approach in comparison with the other flexible parametric and nonparametric approaches through extensive simulation studies. We also compare the proposed method with the other competitive methods with respect to a real-life data set. Though emphasis is put on the logistic regression model the proposed method is applicable to the other members of the generalized linear models, and other types of non-linear regression models too. Finally, we develop a new computational technique to approximate the large sample bias that my arise due to exposure model misspecification in the estimation of the regression parameters in a measurement error scenario.
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Verssani, Bruna Aparecida Wruck. "Modelo de regressão para sistemas reparáveis: um estudo da confiabilidade de colhedoras de cana-de-açúcar." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/11/11134/tde-22012019-173525/.

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A análise de confiabilidade desempenha um papel fundamental para estudos de durabilidade e otimização de tempos de reparo em sistemas reparáveis. Equipamentos como colhedoras de cana-de-açúcar que após a falha e um reparo voltam a exercer sua função objetivo são classificados como sistemas reparáveis. O objetivo deste trabalho consistiu em propor alternativas de modelagem para sistemas complexos, que apresentam grande variabilidade no comportamento da função intensidade de falha. Foi proposta a nova distribuição odd log-logística Weibull flexível generalizada (GOLLFW) e um modelo de regressão Weibull aplicado ao processo lei de potência usado para analisar sistemas reparáveis. Para a nova distribuição foi apresentada a família de distribuições odd log-logística generalizada, realizado um estudo de simulação para verificar algumas propriedades dos estimadores de máxima verossimilhança e incluídas covariáveis na análise dos tempos de falha através do modelo de regressão GOLLFW. Para a análise de regressão considerando os sistemas reparáveis, foram apresentados os principais modelos de contagem para um único sistema reparável e realizado a análise deles de forma separada e, em seguida, foram considerados mais de dois sistemas e acrescentado um modelo de regressão Weibull ao processo lei de potência (PLP). A característica de bimodalidade da distribuição GOLLFW garantiu a adequabilidade e um melhor ajuste aos dados. Já a inclusão de covariáveis através do modelo de regressão Weibull no PLP permitiu modelar sistemas que antes somente os processos de contagens tradicionais, processo lei de potência e processo de renovação, não se adequariam bem.<br>The confiability analysis carries out an important role for durability studies and optimization of repair time in repairable systems. Repairable systems are equipments that returns to execute its function after a fail, for example, sugarcane harvester. This work aimed to propose modeling alternatives for complex systems with great variability in the behaviour of fail intensity function. It was proposed a new distribution on generalized odd log-logistic flexible Weibull (GOLLFW) and an Weibull regression model applied to potential law used to analyze repairable systems.It was presented the distribution family generalized odd log-logistic, was carried out a simulation study to verify some properties of maximum likelihood estimators and was included covariables in the fail time by regression model GOLLFW. To the regression analysis considering repairable systems, it was presented the main counting models for a single repairable system and it was performed an analysis of each model singly, then, it was considered more than two systems and it was added a Weibull regression model to the potential law process (PLP). The bimodality characteristic of GOLLFW distribution guaranteed the suitability and a better adjust to tested datas. While, the inclusion of covariables by regression model GOLLFW in the PLP allowed to model systems which traditionals counting process, PLP and renewal process, would not fit well.
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Tran, Xuan Quang. "Les modèles de régression dynamique et leurs applications en analyse de survie et fiabilité." Thesis, Bordeaux, 2014. http://www.theses.fr/2014BORD0147/document.

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Cette thèse a été conçu pour explorer les modèles dynamiques de régression, d’évaluer les inférences statistiques pour l’analyse des données de survie et de fiabilité. Ces modèles de régression dynamiques que nous avons considérés, y compris le modèle des hasards proportionnels paramétriques et celui de la vie accélérée avec les variables qui peut-être dépendent du temps. Nous avons discuté des problèmes suivants dans cette thèse.Nous avons présenté tout d’abord une statistique de test du chi-deux généraliséeY2nquiest adaptative pour les données de survie et fiabilité en présence de trois cas, complètes,censurées à droite et censurées à droite avec les covariables. Nous avons présenté en détailla forme pratique deY2nstatistique en analyse des données de survie. Ensuite, nous avons considéré deux modèles paramétriques très flexibles, d’évaluer les significations statistiques pour ces modèles proposées en utilisantY2nstatistique. Ces modèles incluent du modèle de vie accélérés (AFT) et celui de hasards proportionnels (PH) basés sur la distribution de Hypertabastic. Ces deux modèles sont proposés pour étudier la distribution de l’analyse de la duré de survie en comparaison avec d’autre modèles paramétriques. Nous avons validé ces modèles paramétriques en utilisantY2n. Les études de simulation ont été conçus.Dans le dernier chapitre, nous avons proposé les applications de ces modèles paramétriques à trois données de bio-médicale. Le premier a été fait les données étendues des temps de rémission des patients de leucémie aiguë qui ont été proposées par Freireich et al. sur la comparaison de deux groupes de traitement avec des informations supplémentaires sur les log du blanc du nombre de globules. Elle a montré que le modèle Hypertabastic AFT est un modèle précis pour ces données. Le second a été fait sur l’étude de tumeur cérébrale avec les patients de gliome malin, ont été proposées par Sauerbrei &amp; Schumacher. Elle a montré que le meilleur modèle est Hypertabastic PH à l’ajout de cinq variables de signification. La troisième demande a été faite sur les données de Semenova &amp; Bitukov, à concernant les patients de myélome multiple. Nous n’avons pas proposé un modèle exactement pour ces données. En raison de cela était les intersections de temps de survie.Par conséquent, nous vous conseillons d’utiliser un autre modèle dynamique que le modèle de la Simple Cross-Effect à installer ces données<br>This thesis was designed to explore the dynamic regression models, assessing the sta-tistical inference for the survival and reliability data analysis. These dynamic regressionmodels that we have been considered including the parametric proportional hazards andaccelerated failure time models contain the possibly time-dependent covariates. We dis-cussed the following problems in this thesis.At first, we presented a generalized chi-squared test statisticsY2nthat is a convenient tofit the survival and reliability data analysis in presence of three cases: complete, censoredand censored with covariates. We described in detail the theory and the mechanism to usedofY2ntest statistic in the survival and reliability data analysis. Next, we considered theflexible parametric models, evaluating the statistical significance of them by usingY2nandlog-likelihood test statistics. These parametric models include the accelerated failure time(AFT) and a proportional hazards (PH) models based on the Hypertabastic distribution.These two models are proposed to investigate the distribution of the survival and reliabilitydata in comparison with some other parametric models. The simulation studies were de-signed, to demonstrate the asymptotically normally distributed of the maximum likelihood estimators of Hypertabastic’s parameter, to validate of the asymptotically property of Y2n test statistic for Hypertabastic distribution when the right censoring probability equal 0% and 20%.n the last chapter, we applied those two parametric models above to three scenes ofthe real-life data. The first one was done the data set given by Freireich et al. on thecomparison of two treatment groups with additional information about log white blood cellcount, to test the ability of a therapy to prolong the remission times of the acute leukemiapatients. It showed that Hypertabastic AFT model is an accurate model for this dataset.The second one was done on the brain tumour study with malignant glioma patients, givenby Sauerbrei &amp; Schumacher. It showed that the best model is Hypertabastic PH onadding five significance covariates. The third application was done on the data set given by Semenova &amp; Bitukov on the survival times of the multiple myeloma patients. We did not propose an exactly model for this dataset. Because of that was an existing oneintersection of survival times. We, therefore, suggest fitting other dynamic model as SimpleCross-Effect model for this dataset
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Mackenzie, Monique L. "Flexible Mixed Models: Regression Splines and Thin-Plate Regression Splines in a Mixed Model Framework." 2005. http://hdl.handle.net/2292/650.

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Whole document restricted, see Access Instructions file below for details of how to access the print copy.<br>Regression splines and thin-plate regression splines were fitted inside generalized linear mixed models with good results. Their role in prediction and as exploratory tools are examined. Regression splines were specified in advance using biological information and compared with knot positions chosen using the data available. A forwards selection procedure was used to choose knots for thin-plate regression splines, and both cross-validation and fit statistics were used to discriminate between competing models. Parameter bias was assessed using a parametric bootstrap in the generalized mixed model setting, and bias for both high and low variance data was compared. Model-based, bootstrap, and robust inference methods were used to assess parameter inference, and the impact of peculiar individuals on the models were examined. Forestry growth and mortality data is used for the modelling throughout. Model specification using biological information returned good results, and models with a relatively small number of well chosen knots outperformed models with larger numbers of relatively poorly placed knots. The generalized mixed model fixed effects estimates were found to be unbiased, but the model-based variance estimates were consistently too small. While variance estimates for terms with random effects were more realistic, robust measures of inference were consistently more reliable. For the normal errors models, model-based inference was only valid when complex covariance structures were specified or robust inference was used Generalized mixed models were found to be relatively robust to influential individuals while cross-validation enabled problematic individuals to be identified.
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Books on the topic "Flexible regression models"

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Center, Ames Research, ed. On the reliable and flexible solution of practical subset regression problems. National Aeronautics and Space Administration, Ames Research Center, 1987.

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Park, Hyung. Flexible Regression Models for Estimating Interactions between a Treatment and Scalar/Functional Predictors. [publisher not identified], 2018.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda de Bastiani. Flexible Regression and Smoothing. Taylor & Francis Group, 2020.

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Dunson, David. Flexible Bayes regression of epidemiologic data. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.1.

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This article focuses on flexible Bayes regression of epidemiologic data involving pregnancy outcomes. It first provides an overview of finite mixture models and nonparametric Bayes methods before discussing some of the possibilities focusing on gestational age at delivery, DDE and age data from the Longnecker et al. (2001) study. More specifically, it examines how risk of premature delivery is impacted by maternal exposure to the pesticide DDT. The results showcase the use of Bayesian analysis in epidemiological studies that collect continuous health outcomes data, and in which the scientific and clinical interest typically focuses on the relationships between exposures and risks of an abnormal response, corresponding to an observation in the tails of the distribution. The article also highlights the limitations of current standard approaches that can be overcome by means of Bayesian analysis using density regression, mixtures and nonparametric models, as developed and applied in this pregnancy outcome study.
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Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda De Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda De Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Heller, Gillian Z., Vlasios Voudouris, Mikis D. Stasinopoulos, Robert A. Rigby, and Fernanda De Bastiani. Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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Book chapters on the topic "Flexible regression models"

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Au, Charles, and S. T. Boris Choy. "An Application of Bayesian Seemingly Unrelated Regression Models with Flexible Tails." In Springer Proceedings in Mathematics & Statistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54084-9_11.

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Radwan, Mostafa M., Mostafa A. Abo-Hashema, Hamdy P. Faheem, and Mostafa D. Hashem. "ANN-Based Fatigue and Rutting Prediction Models Versus Regression-Based Models for Flexible Pavements." In Recent Developments in Pavement Engineering. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34196-1_9.

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Titterington, D. M. "Optimal Design in Flexible Models, Including Feed-Forward Networks and Nonparametric Regression." In Nonconvex Optimization and Its Applications. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4757-3419-5_23.

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Mantovan, Pietro, and Andrea Pastore. "Flexible Dynamic Regression Models for Real-time Forecasting of Air Pollutant Concentration." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17111-6_22.

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Migliorati, Sonia, Agnese M. Di Brisco, and Andrea Ongaro. "The Flexible Beta Regression Model." In Data Analysis and Applications 1. John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119597568.ch3.

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Kriksciuniene, Dalia, Virgilijus Sakalauskas, Ivana Ognjanović, and Ramo Šendelj. "Discovering Healthcare Data Patterns by Artificial Intelligence Methods." In Intelligent Systems for Sustainable Person-Centered Healthcare. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-79353-1_10.

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AbstractThe variety of the artificial intelligence and machine learning methods are applied for data analysis in various areas, including the data-rich healthcare domain. However, aiming to improve health care efficiency and use the captured information to improve treatment methods is often hampered by poor quality of medical data collections, as high percent of health data are unstructured and preserved in different systems and formats. In addition, it is not always agreed which methods of artificial intelligence and machine learning perform better in different problem areas, and which computer tools could make their application more convenient and flexible. The chapter provides essential characteristics of methods, traditionally applied in statistics, such as regression analysis, as well as their advanced modifications of logit, probit models, K-means, and Neural networks. The performance of the methods, their analytical power and relevance to the healthcare application domain is illustrated by brief experimental computations for investigation of stroke patient database with the help of several readily available software tools, such as MS Excel, Statistica, Matlab, Google BigQuery ML.
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Jung, Yu-Jin, and Yong-Ik Yoon. "Flexible Multi-level Regression Model for Prediction of Pedestrian Abnormal Behavior." In Advances in Parallel and Distributed Computing and Ubiquitous Services. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0068-3_17.

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Antosz, Katarzyna. "Prediction Model of Product Quality in Production Company: Based on PCA and Logistic Regression." In Flexible Automation and Intelligent Manufacturing: Establishing Bridges for More Sustainable Manufacturing Systems. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-38165-2_50.

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Miao, Yinsen, Jeong Hwan Kook, Yadong Lu, Michele Guindani, and Marina Vannucci. "Scalable Bayesian variable selection regression models for count data." In Flexible Bayesian Regression Modelling. Elsevier, 2020. http://dx.doi.org/10.1016/b978-0-12-815862-3.00015-9.

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Rahman, Mohammad Arshad, and Shubham Karnawat. "Flexible Bayesian Quantile Regression in Ordinal Models." In Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part B. Emerald Publishing Limited, 2019. http://dx.doi.org/10.1108/s0731-90532019000040b011.

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Conference papers on the topic "Flexible regression models"

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Ryseck, Peter, Racheal Erhard, Michael Cunningham, Feyyaz Guener, Monica Londono, and Zouhair Mahboubi. "Gaussian Process Surrogate Model for eVTOL Propeller Aerodynamics." In Vertical Flight Society 81st Annual Forum and Technology Display. The Vertical Flight Society, 2025. https://doi.org/10.4050/f-0081-2025-397.

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Gaussian Process Regression (GPR) is a flexible, non-parametric machine learning method well-suited for regression tasks. In the context of modeling aerodynamic propellers, GPR significantly reduces the amount of computationally expensive training data needed compared to simpler interpolation or curve-fitting approaches for the same level of accuracy. This work explores several strategies for building a surrogate model of an isolated propeller for the Joby Aviation tilt-propeller electric vertical take-off and landing (eVTOL) aircraft. To better capture sharp local variations in output quantities of interest and accommodate unevenly spaced training data, a novel delta-layer GPR approach is introduced. This method builds on the traditional single-layer GPR method by fitting to the error between the training data and the first layer fit. In parallel, a multi-fidelity GPR model is developed, using lower-fidelity data to achieve better prediction of the underlying mean function while incorporating high-fidelity CFD data for precision. This approach is further extended by integrating a third source of high-fidelity wind tunnel data, resulting in a smooth and accurate surrogate model across the entire flight envelope.
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Božić, Dubravka, Biserka Runje, Andrej Razumić, Dragutin Lisjak, and Branko Strbac. "RISK ASSESSMENT FOR LINEAR REGRESSION MODELS IN METROLOGY: HYPOTHETICAL CASES." In XV INTERNATIONAL SCIENTIFIC CONFERENCE MMA 2024 – Flexible Technologies. Faculty of Technical Sciences, 2024. http://dx.doi.org/10.24867/mma-2024-03-010.

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Okuno, Alex, and Alberto Ferreira. "Generalized linear tree: a flexible algorithm for predicting continuous variables." In LatinX in AI at International Conference on Machine Learning 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai2021072420.

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Tree-based models are popular among regression methods to predict continuous variables. Also, Generalized Linear Models (GLMs) are pretty standard in many statistical applications and provide a generalization to many of the most commonly applied statistical procedures. However, in most regression tree methods, there is only one theoretical model associated for prediction in the final nodes, like multiple linear regression, logistic regressions, polynomial models, Poisson models, among others. We, therefore, propose a new tree method in which we estimate a GLM in each leaf node of the estimated tree including variable selection, new hyperparameters optimization, and tree pruning. Our method, called Generalized linear tree (GLT), has shown to be competitive compared to other well-known regression methods in real datasets, with the advantages and estimation flexibility provided by GLMs.
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Usta, I. "Robust regression models based on flexible maximum entropy distributions." In International Conference on Quality, Reliability, Risk, Maintenance and Safety Engineering, edited by Y. M. Kantar. WIT Press, 2015. http://dx.doi.org/10.2495/qr2mse140421.

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Gupta, Ashish Kumar, Nivedita Naik, and Amol D. Rahulkar. "A Comparative Study of Regression Models for SoC Estimation in Electric Vehicle." In 2023 5th International Conference on Energy, Power and Environment: Towards Flexible Green Energy Technologies (ICEPE). IEEE, 2023. http://dx.doi.org/10.1109/icepe57949.2023.10201550.

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Mendes de Sousa, José Renato, and Anderson Cunha dos Santos. "Symbolic Regression Equations to Predict the Maximum Stress Concentration Factors in Flexible Pipes With Damaged Tensile Armors." In ASME 2024 43rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/omae2024-122906.

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Abstract Flexible pipes are fundamental in various offshore oil and gas exploration applications, such as production, gas injection, gas lift, or water injection lines. However, such structures can be damaged infield, reducing their structural capacity and leading to costs associated with replacements, production loss, or oil leakage. Among the various failure modes of flexible pipes, the rupture of the tensile armors is critical, as these armors withstand the axial loads imposed on the pipe. In this context, this work aims to contribute to evaluating the structural integrity of damaged flexible pipes subjected to tensile loads. Given the limitations of analytical models and the high computational cost of models based on the finite element (FE) method in assessing the structural behavior of damaged flexible pipelines, this work proposes an empirical-analytical equation obtained through Symbolic Regression (SR) as an expedited alternative for determining the maximum stresses in the tensile armors of damaged flexible pipes. Firstly, several flexible pipes were analyzed using a previously proposed FE model. Several tensile armors in their outer layers were considered broken in these analyses. The maximum stress concentration factors (SCF) in the remaining tensile armors were calculated and, together with nondimensional parameters selected to represent the pipe’s mechanical response adequately, formed a dataset analyzed with an SR tool. Finally, a closed-form equation (SR equation) was obtained to analyze other damaged flexible pipes. The proposed equation was verified against FE model results and experimental tests, and good agreement was shown.
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Maseda, Tomé, Jonatan Enes, Roberto R. Expósito, and Juan Touriño. "CPUPowerWatcher/Seer: Automated and Flexible CPU Power Modelling." In VII Congreso XoveTIC: impulsando el talento científico. Servizo de Publicacións. Universidade da Coruña, 2024. https://doi.org/10.17979/spudc.9788497498913.44.

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Power supply is a key limitation when scaling supercomputing capabilities, making power consumption a major challenge in HPC field. To develop energy-efficient tools, it is essential to have an accurate power consumption modelling. Although previous works proposed several approaches to model CPU power, building models in an automated and adaptable way, and accurately predicting power, remains complex. This work presents two tools: CPUPowerWatcher, which gathers CPU metrics during the execution of user-defined workloads, and CPUPowerSeer, which build models to predict power from time series data. Using both tools, experiments were conducted to analyse the impact of novel factors on CPU power and compare the accuracy of six regression models when predicting CPU- and I/O-intensive workloads.
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Gonzalez, Gabriel M., José Renato M. de Sousa, Luis V. S. Sagrilo, Ricardo R. Martins, and Djalene M. Rocha. "A Symbolic Regression Formulation to Estimate the Lateral Buckling Resistance of Tensile Armors in Flexible Pipes." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95510.

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Abstract In this work, a previously proposed finite element is applied in conjunction with a modal approach to predict the lateral buckling resistance of the tensile armors in flexible pipes. The finite element represents the mechanical behavior of tensile armors settled on elastic foundations, which model the frictional interaction between these armors and the surrounding layers. This FE modal approach is used to evaluate the buckling response of 44 different tensile armors considering 15 different friction coefficients between layers. The responses obtained formed a dataset employed in symbolic regression analyses that led to an analytical formulation capable of adequately reproducing the numerical results with minimum computational effort. The results obtained with this analytical formulation are compared to those from other numerical models and experimental measurements showing good agreement and evidencing the potential of the proposed formulation.
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Tsiligaridis, John. "Approaches of Classification Models for Sentiment Analysis." In 5th International Conference on Advanced Natural Language Processing. Academy & Industry Research Collaboration Center, 2024. http://dx.doi.org/10.5121/csit.2024.141007.

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Sentiment analysis (SA) is a Natural Language Processing (NLP) method that helps identify the emotions in text. It is the automated process of identifying and classifying emotions in a text as positive, negative, or neutral sentiment. This way, companies can understand customers’ sentiments, improve their products and services accordingly, and determine effective strategies. The need to discover the algorithm with the best classification performance is obvious. To this end, two different approaches for Sentiment Analysis problems are presented. The first one is based on Machine Learning (ML) models and the second one on Deep Learning (DP) models. Most ML models are flexible depending on their classifier hyperparameters and provide competitive accuracy levels but not all of them. Logistic Regression (LR), Random Forests (RF) of ML and the various models based on Neural Networks (NNs) of DL are applied. Useful results are obtained. Measures for classifiers’ effectiveness are also provided.
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Hatakeyama, Waku, Cong Wang, and Lu Lu. "Nonparametric Tool Path Compensation for Machining Flexible Parts." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9640.

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This paper discusses the compensation of tool paths for machining flexible parts. Despite various research published on the topic, machining in practice nowadays remains limited to tool path planning based on only the geometric models of the parts and tools. This is mainly because that tool path compensation methods usually require accurate physical information of the systems and rely on analytical or finite element simulations, which are often not available to the end-users. In regards to this problem, this paper presents data-oriented nonparametric learning methods that require solely the geometric measurements of the trial machined contour(s). The physical parameters of the parts and tools as well as simulations of the machining process are not required. Two algorithms are developed based on Gaussian Process Regression and Artificial Neural Network respectively. Experimental tests are conducted. A plan of further improving the results using an auxiliary real-time vision sensor is also discussed.
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Reports on the topic "Flexible regression models"

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Cattaneo, Matias D., Richard K. Crump, Max H. Farrell, and Yingjie Feng. Nonlinear Binscatter Methods. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1110.

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Binned scatter plots are a powerful statistical tool for empirical work in the social, behavioral, and biomedical sciences. Available methods rely on a quantile-based partitioning estimator of the conditional mean regression function to primarily construct flexible yet interpretable visualization methods, but they can also be used to estimate treatment effects, assess uncertainty, and test substantive domain-specific hypotheses. This paper introduces novel binscatter methods based on nonlinear, possibly nonsmooth M-estimation methods, covering generalized linear, robust, and quantile regression models. We provide a host of theoretical results and practical tools for local constant estimation along with piecewise polynomial and spline approximations, including (i) optimal tuning parameter (number of bins) selection, (ii) confidence bands, and (iii) formal statistical tests regarding functional form or shape restrictions. Our main results rely on novel strong approximations for general partitioning-based estimators covering random, data-driven partitions, which may be of independent interest. We demonstrate our methods with an empirical application studying the relation between the percentage of individuals without health insurance and per capita income at the zip-code level. We provide general-purpose software packages implementing our methods in Python, R, and Stata.
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Lu, Tianjun, Jian-yu Ke, Fynnwin Prager, and Jose N. Martinez. “TELE-commuting” During the COVID-19 Pandemic and Beyond: Unveiling State-wide Patterns and Trends of Telecommuting in Relation to Transportation, Employment, Land Use, and Emissions in Calif. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2147.

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Telecommuting, the practice of working remotely at home, increased significantly (25% to 35%) early in the COVID-19 pandemic. This shift represented a major societal change that reshaped the family, work, and social lives of many Californians. These changes also raise important questions about what factors influenced telecommuting before, during, and after COVID-19, and to what extent changes in telecommuting have influenced transportation patterns across commute modes, employment, land use, and environment. The research team conducted state-level telecommuting surveys using a crowd-sourced platform (i.e., Amazon Mechanical Turk) to obtain valid samples across California (n=1,985) and conducted state-level interviews among stakeholders (n=28) across ten major industries in California. The study leveraged secondary datasets and developed regression and time-series models. Our surveys found that, compared to pre-pandemic levels, more people had a dedicated workspace at home and had received adequate training and support for telecommuting, became more flexible to choose their own schedules, and had improved their working performance—but felt isolated and found it difficult to separate home and work life. Our interviews suggested that telecommuting policies were not commonly designed and implemented until COVID-19. Additionally, regression analyses showed that telecommuting practices have been influenced by COVID-19 related policies, public risk perception, home prices, broadband rates, and government employment. This study reveals advantages and disadvantages of telecommuting and unveils the complex relationships among the COVID-19 outbreak, transportation systems, employment, land use, and emissions as well as public risk perception and economic factors. The study informs statewide and regional policies to adapt to the new patterns of telecommuting.
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Galili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, 1994. http://dx.doi.org/10.32747/1994.7570549.bard.

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The objectives of this project were to develop nondestructive methods for detection of internal properties and firmness of fruits and vegetables. One method was based on a soft piezoelectric film transducer developed in the Technion, for analysis of fruit response to low-energy excitation. The second method was a dot-matrix piezoelectric transducer of North Carolina State University, developed for contact-pressure analysis of fruit during impact. Two research teams, one in Israel and the other in North Carolina, coordinated their research effort according to the specific objectives of the project, to develop and apply the two complementary methods for quality control of agricultural commodities. In Israel: An improved firmness testing system was developed and tested with tropical fruits. The new system included an instrumented fruit-bed of three flexible piezoelectric sensors and miniature electromagnetic hammers, which served as fruit support and low-energy excitation device, respectively. Resonant frequencies were detected for determination of firmness index. Two new acoustic parameters were developed for evaluation of fruit firmness and maturity: a dumping-ratio and a centeroid of the frequency response. Experiments were performed with avocado and mango fruits. The internal damping ratio, which may indicate fruit ripeness, increased monotonically with time, while resonant frequencies and firmness indices decreased with time. Fruit samples were tested daily by destructive penetration test. A fairy high correlation was found in tropical fruits between the penetration force and the new acoustic parameters; a lower correlation was found between this parameter and the conventional firmness index. Improved table-top firmness testing units, Firmalon, with data-logging system and on-line data analysis capacity have been built. The new device was used for the full-scale experiments in the next two years, ahead of the original program and BARD timetable. Close cooperation was initiated with local industry for development of both off-line and on-line sorting and quality control of more agricultural commodities. Firmalon units were produced and operated in major packaging houses in Israel, Belgium and Washington State, on mango and avocado, apples, pears, tomatoes, melons and some other fruits, to gain field experience with the new method. The accumulated experimental data from all these activities is still analyzed, to improve firmness sorting criteria and shelf-life predicting curves for the different fruits. The test program in commercial CA storage facilities in Washington State included seven apple varieties: Fuji, Braeburn, Gala, Granny Smith, Jonagold, Red Delicious, Golden Delicious, and D'Anjou pear variety. FI master-curves could be developed for the Braeburn, Gala, Granny Smith and Jonagold apples. These fruits showed a steady ripening process during the test period. Yet, more work should be conducted to reduce scattering of the data and to determine the confidence limits of the method. Nearly constant FI in Red Delicious and the fluctuations of FI in the Fuji apples should be re-examined. Three sets of experiment were performed with Flandria tomatoes. Despite the complex structure of the tomatoes, the acoustic method could be used for firmness evaluation and to follow the ripening evolution with time. Close agreement was achieved between the auction expert evaluation and that of the nondestructive acoustic test, where firmness index of 4.0 and more indicated grade-A tomatoes. More work is performed to refine the sorting algorithm and to develop a general ripening scale for automatic grading of tomatoes for the fresh fruit market. Galia melons were tested in Israel, in simulated export conditions. It was concluded that the Firmalon is capable of detecting the ripening of melons nondestructively, and sorted out the defective fruits from the export shipment. The cooperation with local industry resulted in development of automatic on-line prototype of the acoustic sensor, that may be incorporated with the export quality control system for melons. More interesting is the development of the remote firmness sensing method for sealed CA cool-rooms, where most of the full-year fruit yield in stored for off-season consumption. Hundreds of ripening monitor systems have been installed in major fruit storage facilities, and being evaluated now by the consumers. If successful, the new method may cause a major change in long-term fruit storage technology. More uses of the acoustic test method have been considered, for monitoring fruit maturity and harvest time, testing fruit samples or each individual fruit when entering the storage facilities, packaging house and auction, and in the supermarket. This approach may result in a full line of equipment for nondestructive quality control of fruits and vegetables, from the orchard or the greenhouse, through the entire sorting, grading and storage process, up to the consumer table. The developed technology offers a tool to determine the maturity of the fruits nondestructively by monitoring their acoustic response to mechanical impulse on the tree. A special device was built and preliminary tested in mango fruit. More development is needed to develop a portable, hand operated sensing method for this purpose. In North Carolina: Analysis method based on an Auto-Regressive (AR) model was developed for detecting the first resonance of fruit from their response to mechanical impulse. The algorithm included a routine that detects the first resonant frequency from as many sensors as possible. Experiments on Red Delicious apples were performed and their firmness was determined. The AR method allowed the detection of the first resonance. The method could be fast enough to be utilized in a real time sorting machine. Yet, further study is needed to look for improvement of the search algorithm of the methods. An impact contact-pressure measurement system and Neural Network (NN) identification method were developed to investigate the relationships between surface pressure distributions on selected fruits and their respective internal textural qualities. A piezoelectric dot-matrix pressure transducer was developed for the purpose of acquiring time-sampled pressure profiles during impact. The acquired data was transferred into a personal computer and accurate visualization of animated data were presented. Preliminary test with 10 apples has been performed. Measurement were made by the contact-pressure transducer in two different positions. Complementary measurements were made on the same apples by using the Firmalon and Magness Taylor (MT) testers. Three-layer neural network was designed. 2/3 of the contact-pressure data were used as training input data and corresponding MT data as training target data. The remaining data were used as NN checking data. Six samples randomly chosen from the ten measured samples and their corresponding Firmalon values were used as the NN training and target data, respectively. The remaining four samples' data were input to the NN. The NN results consistent with the Firmness Tester values. So, if more training data would be obtained, the output should be more accurate. In addition, the Firmness Tester values do not consistent with MT firmness tester values. The NN method developed in this study appears to be a useful tool to emulate the MT Firmness test results without destroying the apple samples. To get more accurate estimation of MT firmness a much larger training data set is required. When the larger sensitive area of the pressure sensor being developed in this project becomes available, the entire contact 'shape' will provide additional information and the neural network results would be more accurate. It has been shown that the impact information can be utilized in the determination of internal quality factors of fruit. Until now,
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