Relevant bibliographies by topics / Flexible regression models

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  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: 10 March 2023

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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 (April 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 (July 11, 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 (August 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 (June 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 (May 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 (April 23, 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 (April 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 (October 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 (July 2, 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.
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.
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.
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 & 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 & 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
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 & 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 & 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.
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. Moffett Field, Calif: 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. [New York, N.Y.?]: [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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Flexible Regression and Smoothing: Using GAMLSS in R. Taylor & Francis Group, 2017.

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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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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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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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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, 115–25. Cham: 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, 117–33. Cham: 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, 261–73. Boston, MA: 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, 265–76. Berlin, Heidelberg: 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, 39–52. Hoboken, NJ, USA: 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, 185–210. Cham: 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, 137–43. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0068-3_17.

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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, 187–219. 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, 211–51. Emerald Publishing Limited, 2019. http://dx.doi.org/10.1108/s0731-90532019000040b011.

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J., Jagan, Pijush Samui, and Barnali Dixon. "Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR." In Advances in Environmental Engineering and Green Technologies, 1–18. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9723-2.ch001.

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The prediction of medical waste generation is an important task in hospital waste management. This article uses Relevance Vector Machine (RVM), Multivariate Adaptive Regression Spline (MARS) and Minimax Probability Machine Regression (MPMR) for prediction of rate of medical waste generation. Type of hospital, Capacity and Bed Occupancy has been used as inputs of RVM, MARS and MPMR. RVM is a probabilistic bayesian learning framework. MARS builds flexible model by using piecewise linear regressions. MPMR maximizes the minimum probability that future predicted outputs of the regression model will be within some bound of the true regression function. MARS, RVM and MPMR have been used as regression techniques. The results show that the developed RVM, MPMR and MARS give excellent models for determination of rate of medical waste generation.
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Conference papers on the topic "Flexible regression models"

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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. Southampton, UK: WIT Press, 2015. http://dx.doi.org/10.2495/qr2mse140421.

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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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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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de Sousa, José Renato M., Marcelo K. Protasio, and Luis V. S. Sagrilo. "Equivalent Layer Approaches to Predict the Bisymmetric Hydrostatic Collapse Strength of Flexible Pipes." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78146.

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The hydrostatic collapse strength of a flexible pipe is largely dependent on the ability of its carcass and pressure armor to resist radial loading and, therefore, its prediction involves an adequate modeling of these layers. Hence, initially, this work proposes a set of equations to estimate equivalent thicknesses and physical properties for these layers, which allows their modeling as equivalent orthotropic cylinders. These equations are obtained by simulating several two-point static ring tests with a three-dimensional finite element (FE) model based on beam elements and using these results to form datasets that are analyzed with a symbolic regression (SR) tool. The results of these analyses are the closed-form equations that best fit the provided datasets. After that, these equations are used in conjunction with a three-dimensional shell FE model and a previously presented analytical model to study the dry and wet hydrostatic collapse mechanisms of a flexible pipe. The predictions of these models agreed quite well with the collapse pressures obtained in experimental tests thus indicating that the use of the equivalent approach is promising.
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Kocher, Lyle, Ed Koeberlein, D. G. Van Alstine, Karla Stricker, and Greg Shaver. "Physically-Based Volumetric Efficiency Model for Diesel Engines Utilizing Variable Intake Valve Actuation." In ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control. ASMEDC, 2011. http://dx.doi.org/10.1115/dscc2011-5997.

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Advanced diesel engine architectures employing flexible valve trains enable emissions reductions and fuel economy improvements. Flexibility in the valve train allows engine designers to optimize the gas exchange process in a manner similar to how common rail fuel injection systems enable optimization of the fuel injection process. Modulating valve timings directly impacts the volumetric efficiency of the engine. In fact, the control authority of valve timing modulation over volumetric efficiency is three times larger than that due to any other engine actuator. Traditional empirical or regression-based models for volumetric efficiency, while suitable for conventional valve trains, are therefore challenged by flexible valve trains. The added complexity and additional empirical data needed for wide valve timing ranges limit the usefulness of these methods. A physically-based volumetric efficiency model was developed to address these challenges. The model captures the major physical processes occurring over the intake stroke, and is applicable to both conventional and flexible valve trains. The model inputs include temperature and pressure in the intake and exhaust manifolds, intake and exhaust valve timings, bore, stoke, connecting rod length, engine speed and effective compression ratio, ECR. The model is physically-based, requires no regression tuning parameters, is generalizable to other engine platforms, and has been experimentally validated using an advanced multi-cylinder diesel engine equipped with a flexible variable intake valve actuation system. Experimental data was collected over a wide range of the operating space of the engine and augmented with air handling actuator and intake valve timing sweeps to maximize the range of conditions used to thoroughly experimentally validate the model for a total of 217 total operating conditions. The physical model developed differs from previous physical modeling work through the novel application of ECR, incorporation of no tuning parameters and extensive validation on unique engine test bed with flexible intake valve actuation.
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Lin, Tao, Mokhles Mezghani, Chicheng Xu, and Weichang Li. "Machine Learning for Multiple Petrophysical Properties Regression Based on Core Images and Well Logs in a Heterogenous Reservoir." In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206089-ms.

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Abstract Reservoir characterization requires accurate prediction of multiple petrophysical properties such as bulk density (or acoustic impedance), porosity, and permeability. However, it remains a big challenge in heterogeneous reservoirs due to significant diagenetic impacts including dissolution, dolomitization, cementation, and fracturing. Most well logs lack the resolution to obtain rock properties in detail in a heterogenous formation. Therefore, it is pertinent to integrate core images into the prediction workflow. This study presents a new approach to solve the problem of obtaining the high-resolution multiple petrophysical properties, by combining machine learning (ML) algorithms and computer vision (CV) techniques. The methodology can be used to automate the process of core data analysis with a minimum number of plugs, thus reducing human effort and cost and improving accuracy. The workflow consists of conditioning and extracting features from core images, correlating well logs and core analysis with those features to build ML models, and applying the models on new cores for petrophysical properties predictions. The core images are preprocessed and analyzed using color models and texture recognition, to extract image characteristics and core textures. The image features are then aggregated into a profile in depth, resampled and aligned with well logs and core analysis. The ML regression models, including classification and regression trees (CART) and deep neural network (DNN), are trained and validated from the filtered training samples of relevant features and target petrophysical properties. The models are then tested on a blind test dataset to evaluate the prediction performance, to predict target petrophysical properties of grain density, porosity and permeability. The profile of histograms of each target property are computed to analyze the data distribution. The feature vectors are extracted from CV analysis of core images and gamma ray logs. The importance of each feature is generated by CART model to individual target, which may be used to reduce model complexity of future model building. The model performances are evaluated and compared on each target. We achieved reasonably good correlation and accuracy on the models, for example, porosity R2=49.7% and RMSE=2.4 p.u., and logarithmic permeability R2=57.8% and RMSE=0.53. The field case demonstrates that inclusion of core image attributes can improve petrophysical regression in heterogenous reservoirs. It can be extended to a multi-well setting to generate vertical distribution of petrophysical properties which can be integrated into reservoir modeling and characterization. Machine leaning algorithms can help automate the workflow and be flexible to be adjusted to take various inputs for prediction.
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Azarkhail, M., and M. Modarres. "A Novel Bayesian Framework for Uncertainty Management in Physics-Based Reliability Models." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41333.

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The physics-of-failure (POF) modeling approach is a proven and powerful method to predict the reliability of mechanical components and systems. Most of POF models have been originally developed based upon empirical data from a wide range of applications (e.g. fracture mechanics approach to the fatigue life). Available curve fitting methods such as least square for example, calculate the best estimate of parameters by minimizing the distance function. Such point estimate approaches, basically overlook the other possibilities for the parameters and fail to incorporate the real uncertainty of empirical data into the process. The other important issue with traditional methods is when new data points become available. In such conditions, the best estimate methods need to be recalculated using the new and old data sets all together. But the original data sets, used to develop POF models may be no longer available to be combined with new data in a point estimate framework. In this research, for efficient uncertainty management in POF models, a powerful Bayesian framework is proposed. Bayesian approach provides many practical features such as a fair coverage of uncertainty and the updating concept that provide a powerful means for knowledge management, meaning that the Bayesian models allow the available information to be stored in a probability density format over the model parameters. These distributions may be considered as prior to be updated in the light of new data when they become available. At the first part of this article a brief review of classical and probabilistic approach to regression is presented. In this part the accuracy of traditional normal distribution assumption for error is examined and a new flexible likelihood function is proposed. The Bayesian approach to regression and its bonds with classical and probabilistic methods are explained next. In Bayesian section we shall discuss how the likelihood functions introduced in probabilistic approach, can be combined with prior information using the conditional probability concept. In order to highlight the advantages, the Bayesian approach is further clarified with case studies in which the result of calculation is compared with other traditional methods such as least square and maximum likelihood estimation (MLE) method. In this research, the mathematical complexity of Bayesian inference equations was overcome utilizing Markov Chain Monte Carlo simulation technique.
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Yılmaz, Yavuz, Rainer Kurz, Ayşe Özmen, and Gerhard-Wilhelm Weber. "A New Algorithm for Scheduling Condition-Based Maintenance of Gas Turbines." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-43545.

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In developed electricity markets, the deregulation boosted competition among companies participating in the electricity market. Therefore, the enhanced reliability and availability of gas turbine systems is an industry obligation. Not only providing the available power with minimum operation and maintenance costs, but also guaranteeing high efficiency are additional requisites and efficiency loss of the power plants leads to a loss of money for the electricity generation companies. Multivariate Adaptive Regression Spline (MARS) is a modern methodology of statistical learning, data mining and estimation theory that is significant in both regression and classification is a form of flexible non-parametric regression analysis capable of modeling complex data. In this study, single shaft, 6MW class industrial gas turbines located at various sites have been monitored. The performance monitoring of a gas turbine consisted of hourly measurements of various input variables over an extended period of time. Using such measurements, predictive models for gas turbine heat rate and the gas turbine axial compressor discharge pressure values have been generated. The measured values have been compared with the values obtained as a result of the MARS models. The MARS-based models are obtained with the combination of gas turbine performance input and target variables and the complementary meteorological data. The results are presented, discussed, and conclusions are drawn for modern energy and cost efficient gas turbine and power plant maintenance management as the outcomes of this study.
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Shui, Huanyi, Xiaoning Jin, and Jun Ni. "Roll-to-Roll Manufacturing System Modeling and Analysis by Stream of Variation Theory." In ASME 2016 11th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/msec2016-8722.

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A multistage system that consists of multiple stages for sequential operations to finish products is widely employed in modern manufacturing systems. Due to the characteristics of multistage systems, the product quality not only depends on operations in current stage but is also affected by operations in upstream stages. Most existing studies use Stream of Variation models to analyze error propagation and interactions among multiple stages in discrete manufacturing systems such as machining shops and assembly systems. In this paper, a multistage model based on the “Stream of Variation” concept is developed to investigate the tension propagation in a flexible material roll-to-roll manufacturing system. This modeling method uses a physical model coupled with a data-driven model to correlate the roller operation performance and product quality characteristics. Torque equilibrium analysis and Hooke’s law are employed for physical model and the censored regression model is used to explore unknown structures/parameters. A web unwinding process demonstrates the feasibility and prediction performance of the proposed model. The result shows that the proposed multistage model can serve as a virtual metrology method to increase system visibility, enhance health management capability and eventually improve product quality.
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Reports on the topic "Flexible regression models"

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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, August 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, October 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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