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Relevant bibliographies by topics / Polynomial regression model

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Academic literature on the topic 'Polynomial regression model'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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Journal articles on the topic "Polynomial regression model"

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Chun-jie, Li, Li Hong-bo, Li Hua, Zhang Li-ming, Fu Chun-yan, and Guo Ji-ping. "Research on Polynomial Regression Prefetching Model." IOP Conference Series: Earth and Environmental Science 332 (November 5, 2019): 022050. http://dx.doi.org/10.1088/1755-1315/332/2/022050.

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Zhang, Tao, Qingzhao Zhang, and Qihua Wang. "Model detection for functional polynomial regression." Computational Statistics & Data Analysis 70 (February 2014): 183–97. http://dx.doi.org/10.1016/j.csda.2013.09.007.

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Suparman, Suparman, and Mohd Saifullah Rusiman. "Bootstrap-based model selection in subset polynomial regression." International Journal of Advances in Intelligent Informatics 4, no. 2 (2018): 87. http://dx.doi.org/10.26555/ijain.v4i2.173.

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The subset polynomial regression model is wider than the polynomial regression model. This study proposes an estimate of the parameters of the subset polynomial regression model with unknown error and distribution. The Bootstrap method is used to estimate the parameters of the subset polynomial regression model. Simulated data is used to test the performance of the Bootstrap method. The test results show that the bootstrap method can estimate well the parameters of the subset polynomial regression model.

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Zhang, Xuan Hao. "Multi-Response Linear Polynomial Regression Models Designs." Applied Mechanics and Materials 380-384 (August 2013): 1314–17. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1314.

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The multi-response linear polynomial model is transformed into a single response polynomial model. In symmetric test area, for the polynomial models the A-D- and E-optimal experimental designs are obtained.

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Zhang, Zhongheng. "Multivariable fractional polynomial method for regression model." Annals of Translational Medicine 4, no. 9 (2016): 174. http://dx.doi.org/10.21037/atm.2016.05.01.

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Royston, Patrick. "Model Selection for Univariable Fractional Polynomials." Stata Journal: Promoting communications on statistics and Stata 17, no. 3 (2017): 619–29. http://dx.doi.org/10.1177/1536867x1701700305.

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Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429–467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp and fp_select to select such models with real data.

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Van Damme, Christopher, Alecio Madrid, Matthew Allen, and Joseph Hollkamp. "Simultaneous Regression and Selection in Nonlinear Modal Model Identification." Vibration 4, no. 1 (2021): 232–47. http://dx.doi.org/10.3390/vibration4010016.

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High fidelity finite element (FE) models are widely used to simulate the dynamic responses of geometrically nonlinear structures. The high computational cost of running long time duration analyses, however, has made nonlinear reduced order models (ROMs) attractive alternatives. While there are a variety of reduced order modeling techniques, in general, their shared goal is to project the nonlinear response of the system onto a smaller number of degrees of freedom. Implicit Condensation (IC), a popular and non-intrusive technique, identifies the ROM parameters by fitting a polynomial model to static force-displacement data from FE model simulations. A notable drawback of these models, however, is that the number of polynomial coefficients increases cubically with the number of modes included within the basis set of the ROM. As a result, model correlation, updating and validation become increasingly more expensive as the size of the ROM increases. This work presents simultaneous regression and selection as a method for filtering the polynomial coefficients of a ROM based on their contributions to the nonlinear response. In particular, this work utilizes the method of least absolute shrinkage and selection (LASSO) to identify a sparse set of ROM coefficients during the IC regression step. Cross-validation is used to demonstrate accuracy of the sparse models over a range of loading conditions.

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Jēkabsons, Gints, and Jurijs Lavendels. "A comparison of subset selection and adaptive basis function construction for polynomial regression model building." Scientific Journal of Riga Technical University. Computer Sciences 38, no. 38 (2009): 187–97. http://dx.doi.org/10.2478/v10143-009-0017-7.

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A comparison of subset selection and adaptive basis function construction for polynomial regression model buildingThe approach of subset selection in polynomial regression model building assumes that the chosen fixed full set of predefined basis functions contains a subset that is sufficient to describe the target relation sufficiently well. However, in most cases the necessary set of basis functions is not known and needs to be guessed - a potentially non-trivial (and long) trial and error process. In our previous research we considered an approach for polynomial regression model building which is different from the subset selection - letting the regression model building method itself construct the basis functions necessary for creating a model of arbitrary complexity without restricting oneself to the basis functions of a predefined full model. The approach is titled Adaptive Basis Function Construction (ABFC). In the present paper we compare the two approaches for polynomial regression model building - subset selection and ABFC - both theoretically and empirically in terms of their underlying principles, computational complexity, and predictive performance. Additionally in empirical evaluations the ABFC is compared also to two other well-known regression modelling methods - Locally Weighted Polynomials and Multivariate Adaptive Regression Splines.

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Nur’eni, M. Fajri, and S. Astuti. "Comparison of Kernel regression model with a polynomial regression model on financial data." Journal of Physics: Conference Series 1763, no. 1 (2021): 012017. http://dx.doi.org/10.1088/1742-6596/1763/1/012017.

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Sauerbrei, W., and P. Royston. "Building Multivariable Regression Models with Continuous Covariates in Clinical Epidemiology." Methods of Information in Medicine 44, no. 04 (2005): 561–71. http://dx.doi.org/10.1055/s-0038-1634008.

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Summary Objectives: In fitting regression models, data analysts must often choose a model based on several candidate predictor variables which may influence the outcome. Most analysts either assume a linear relationship for continuous predictors, or categorize them and postulate step functions. By contrast, we propose to model possible non-linearity in the relationship between the outcome and several continuous predictors by estimating smooth functions of the predictors. We aim to demonstrate that a structured approach based on fractional polynomials can give a broadly satisfactory practical solution to the problem of simultaneously identifying a subset of 'important' predictors and determining the functional relationship for continuous predictors. Methods: We discuss the background, and motivate and describe the multivariable fractional polynomial (MFP) approach to model selection from data which include continuous and categorical predictors. We compare our results with those from other approaches in examples. We present a small simulation study to compare the functional form of the relationship obtained by fitting fractional polynomials and splines to a single predictor variable. Results: We illustrate the advantages of the MFP approach over standard techniques of model construction in two real example datasets analyzed with logistic and Cox regression models, respectively. In the simulation study, fractional polynomial models had lower mean square error and more realistic behaviour than comparable spline models. Conclusions: In many practical situations, the MFP approach can satisfy the aim of finding models that fit the data well and also are simple, interpretable and potentially transportable to other settings.

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Dissertations / Theses on the topic "Polynomial regression model"

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Smith, J. R. "Design of experiments for the precise estimation of the optimum, economic optimim and parameters for one factor inverse polynomial models." Thesis, University of Reading, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.380109.

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Kracík, Adam. "Matematický model rozložení tvrdosti na opěrném válci." Doctoral thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2011. http://www.nusl.cz/ntk/nusl-233957.

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The aim of this work is to get the best detailed knowledge about hardness distribution in first 60 mm below the surface of backing roll. To this end, a method for obtaining multi-dimensional polynomial regression was developed and then a computer program for its processing was written.Way of finding suitable regression surfaces and their subsequent interpretation, is a pivotal part of this work.

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Van, Deventer Megan. "The development and empirical evaluation of an work engagement structural model." Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/96784.

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Thesis (MComm)--Stellenbosch University, 2015.<br>ENGLISH ABSTRACT: Work Engagement is one construct of many that forms part of the complex nomological network of constructs underlying the behaviour of working man2. Work Engagement is an important construct both from an individual as well as from an organisational perspective. Human resource management interventions aimed at enhancing Work Engagement aspire to contribute to the achievement of the organisation’s primary objective and the well-being of the organisation’s employees. Such interventions will most likely also be valued by individuals within the workplace, as individuals will be able to experience a sense of personal fulfilment through self-expression at work. It is therefore essential to gain a valid understanding of the Work Engagement construct and the psychological mechanism that underpins it, in order to design human resource interventions that will successfully enhance Work Engagement. The current study raises the question why variance in Work Engagement exists amongst different employees working in different organisational contexts. The research objective of the current study is to develop and empirically test an explanatory Work Engagement structural model that will provide a valid answer to this question. In this study, a comprehensive Work Engagement structural model was proposed. An ex post facto correlational design with structural equation modelling (SEM) as the statistical analysis technique was used to test the substantive research hypotheses as represented by the Work Engagement structural model. Furthermore, the current study tested two additional narrow-focus structural models describing the impact of value congruence on Work Engagement by using an ex post facto correlational design with polynomial regression as the statistical analysis technique. A convenience sample of 227 teachers working in public sector schools falling under the jurisdiction of the Western Cape Education Department (WCED) participated in the study. The comprehensive Work Engagement model achieved reasonable close fit. Support was found for all of the hypothesised theoretical relationships in the Work Engagement structural model, except for the influence of the PsyCap*Job Characteristics interaction effect on Meaningfulness and for three of the five latent polynomial regression terms added in the model in an attempt to derive response surface test values. The response surface analyses findings were mixed. Based on the obtained results, meaningful practical recommendations were derived.<br>AFRIKAANSE OPSOMMING: Werkverbintenis1 is een van ‘n groot verskeidenheid konstrukte wat deel vorm van die komplekse nomologiese netwerk van konstrukte wat die gedrag van die arbeidende mens onderlê. Werkverbintenis word as ‘n belangrike konstruk beskou vanuit ‘n individuele sowel as vanuit ‘n organisatoriese perspektief. Menslike hulpbronbestuurs-intervensies gerig op die bevordering van Werkverbintenis streef daarna om by te dra tot die bereiking van die organisasie se primêre doel sowel as tot die welstand van die organisasie se werknemers. Sodanige intervensies sal waarskynlik ook deur werknemers waardeer word, aangesien sodanige intervensies die kanse verhoog dat individue selfvervulling in hul werk sal ervaar omdat die werk hul die geleentheid bied om hulself in hul werk uit te leef. Dit is gevolglik noodsaaklik om ‘n geldige begrip te ontwikkel van die Werkverbintenis-konstruk en die sielkundige meganisme wat dit onderlê ten einde menslike hulpronbestuurs-intervensies te ontwerp wat suksesvol Werkverbintenis sal bevorder. Die huidige studie stel die vraag aan die orde waarom variansie in Werkverbintenis tussen verskillende werknemers bestaan wat in verskillende organisatoriese kontekste werk. Die navorsingsdoelstelling van die huidige studie is om ‘n verklarende Werkverbintenisstrukturele model te ontwikkel en te toets wat ‘n geldige antwoord op hierdie vraag sal bied. ‘n Omvattende Werkverbintenis strukturele model is in hierdie studie voorgestel. ‘n Ex post facto korrelatiewe ontwerp met strukturele vergelykingsmodellering (SVM) as die statistiese ontledingstegniek is gebruik om die substantiewe navorsingshipotese soos voorgestel deur die Werkverbintenis strukturele model te toets. Die huidige studie het voorts twee addisionele nouer-fokus strukturele modelle getoets wat die impak van waardekongruensie op Werkverbintenis beskryf deur middel van ‘n ex post facto korrelatiewe ontwerp met polinomiese regressie-ontleding as statistiese ontledingstegniek. ‘n Geriefsteekproef van 227 onderwysers wat in openbare skole werksaam is wat onder die beheer van die Wes Kaapse Department van Onderwys val (WKDO) het aan die studie deelgeneem. Die omvattende Werkverbintenis-model het redelik goeie pasgehalte getoon. Steun is gevind vir all die voorgestelde teoretiese verwantskappe in die Werkverbintenis strukturele model, behalwe vir die invloed van die Sielkundige kapitaal*Werk eienskappe-interaksie-effek op Betekenisvolheid en vir drie van die vyf polinomiese latente regressie-terme wat in die model ingesluit is in ‘n poging om responsoppervlakte-waardes af te lei. Gemengde resultate is verkry vir die responsoppervlakte-ontleding. Betekenisvolle praktiese aanbevelings is gemaak op grond van die navorsingsresultate.

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CAI, JIACHENG. "The Energy Efficiency Model of a DC Motor for the Control of HEVs." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284330.

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This thesis studies a DC motor for a racing hybrid electric vehicle (HEV) prototype.The development of optimization-based energy management strategies (EMS) necessitates an accurate quasi-static model of the driving motor, which includes a 2D efficiency map with the torque outputand rotating speed as the inputs. However, a DC motor's efficiency varies a lot at differentoperating points and the efficiency map from the technical manual does not match the various applications in reality.In view of this, this thesis investigates a field testing based quasi-static modeling method to construct the DC motor efficiency map with only portable and brief testing resources. Firstly, a testbench is designed, manufactured, integrated, and configured with necessary accessories. The testbench consists of the motor under test, a braking motor to provide load torque, a servo-amplifier for torque control and sensing, a host computer for data acquisition, and power supplies. Then, a self-contained testing plan is designed by which as many as possible different testing points can be covered based on the braking motor's power limit. After that, the experiments are successively performed on the test bench, and the input electric power along with the output mechanical power at steady state are recorded. Multiple data process methods are explored to analyze the collected testing data. Root mean square (RMS) is used to reduce the measuring variance. Invalid outliers are identified and filtered out based on the residuals. The qualified samples are employed to build up the 2D efficiency map by fourth-degree polynomial regression. Then, three methods, linear, quadratic, and cubic fittings are attempted separately to estimate the relationships between the input power and output torque at different speeds. The results show that the quadratic model is the best option which results in smaller root mean square error (RMSE) and fair computation complexity. To conclude, the quasi-static dynamic model of a DC motor, which includes a 2D efficiency map and the speed-based polynomial expression of input power, can be properly established by a new method relying on less and simpler devices in contrast to those traditional methods. This method bypasses a bulk of tedious modulations on precise motor speed control which is heavily dependent on a high-precision sensor. The formulated 2D efficiency map will effectively support the future development of model-based EMS. The polynomial expression provides a more efficient approach to estimate instantaneous energy efficiency for an embedded system application.<br>Denna avhandling studerar en likströmsmotor för en prototyp av ett elektriskt hybridfordon (HEV) för racing. Utvecklingen av optimeringsbaserade energihanteringsstrategier (EMS) kräver en precis kvasistatisk dynamisk modell av den drivande motorn, som inkluderar en en 2D-karta (effektivetetskarta) som beskriver hur verkningsgraden beror på moment och rotationshastighet. Verkningsgraden hos likströmsmotorn varierar dock mycket beroende på arbetspunkt och verkningsgradskartan från databladen stämmer inte alltid med de olika applikationerna i verkligheten. Givet detta undersöker denna avhandling en fältprovsbaserad kvasistatisk modelleringsmetod för att uppskatta likströmsmotorns effektivitetskarta med endast flyttbara och begränsade testresurser. Till att börja med är en testbänk designad, tillverkad, integrerad och konfigurerad med alla nödvändiga komponenter. Testbänken består av den motor som testas, en bromsmotor för att ge belastningsmoment, en servoförstärkare för vridmomentstyrning och mätning, samt en dator för datainsamling och strömförsörjning. Sedan utformas en fristående testplan som gör att så många olika testpunkter som möjligt kan täckas, baserat på bromsmotorn effektgräns. Därefter utförs experimenten successivt på testbänken där ingående elektrisk effekt och utgående mekanisk effekt mäts i jämviktsläget. Flera olika metoder undersöks för att analysera den insamlade testdatan. Kvadratiskt medelvärde används för att minska variansen i testdatan. Ogiltiga outliers identifieras och filtreras ut baserat på hur mycket de avviker från medelvärdet. De godkända testpunkterna används för att bygga upp 2D-effektivitetskartan genom en fjärde gradens polynom regression. Därefter används tre olika metoder, linjära, kvadratiska och kubiska för att skapa kurvanpassningar genom polynomregression för att beskriva sambandet mellan ingångseffekt och utgångseffekt vid olika hastigheter. Resultaten visar att den kvadratiska metoden är det bästa alternativet eftersom det ger en mindre medelkvadratavvikelse och en hanterbar beräkningskomplexitet. Avslutningsvis kan den kvasistatiska dynamiska modellen för en likströmsmotor, som inkluderar en 2D-effektivitetskarta med det hastighetsbaserade polynomuttrycket för ingångseffekt, skapas av en ny metod som förliter sig på mindre och enklare materiel än traditionella metoder. Denna metod kringår en stor del av den omständiga modulering som precis varvtalsstyrning kräver vilken även är väldigt beroende på högprecisionssensorer. Den formulerade 2D-effektivitetskartan kommer ge betydande stöd till framtida utveckling av modelbaserade energihanteringsstrategier 2 (EMS). Polynomuttrycket ger ett mer effektivt tillvägagångssätt för att uppskatta omedelbar energieffektivitet för en inbäddad systemapplikation.

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Yakkali, Sai Santosh. "Decomposing Residential Monthly Electric Utility Bill Into HVAC Energy Use Using Machine Learning." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin155437406441298.

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Heo, Giseon. "Optimal designs for approximately polynomial regression models." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0010/NQ34778.pdf.

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Hu, Wenbiao. "Applications of Spatio-temporal Analytical Methods in Surveillance of Ross River Virus Disease." Queensland University of Technology, 2005. http://eprints.qut.edu.au/16109/.

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The incidence of many arboviral diseases is largely associated with social and environmental conditions. Ross River virus (RRV) is the most prevalent arboviral disease in Australia. It has long been recognised that the transmission pattern of RRV is sensitive to socio-ecological factors including climate variation, population movement, mosquito-density and vegetation types. This study aimed to assess the relationships between socio-environmental variability and the transmission of RRV using spatio-temporal analytic methods. Computerised data files of daily RRV disease cases and daily climatic variables in Brisbane, Queensland during 1985-2001 were obtained from the Queensland Department of Health and the Australian Bureau of Meteorology, respectively. Available information on other socio-ecological factors was also collected from relevant government agencies as follows: 1) socio-demographic data from the Australia Bureau of Statistics; 2) information on vegetation (littoral wetlands, ephemeral wetlands, open freshwater, riparian vegetation, melaleuca open forests, wet eucalypt, open forests and other bushland) from Brisbane City Council; 3) tidal activities from the Queensland Department of Transport; and 4) mosquito-density from Brisbane City Council. Principal components analysis (PCA) was used as an exploratory technique for discovering spatial and temporal pattern of RRV distribution. The PCA results show that the first principal component accounted for approximately 57% of the information, which contained the four seasonal rates and loaded highest and positively for autumn. K-means cluster analysis indicates that the seasonality of RRV is characterised by three groups with high, medium and low incidence of disease, and it suggests that there are at least three different disease ecologies. The variation in spatio-temporal patterns of RRV indicates a complex ecology that is unlikely to be explained by a single dominant transmission route across these three groupings. Therefore, there is need to explore socio-economic and environmental determinants of RRV disease at the statistical local area (SLA) level. Spatial distribution analysis and multiple negative binomial regression models were employed to identify the socio-economic and environmental determinants of RRV disease at both the city and local (ie, SLA) levels. The results show that RRV activity was primarily concentrated in the northeast, northwest and southeast areas in Brisbane. The negative binomial regression models reveal that RRV incidence for the whole of the Brisbane area was significantly associated with Southern Oscillation Index (SOI) at a lag of 3 months (Relative Risk (RR): 1.12; 95% confidence interval (CI): 1.06 - 1.17), the proportion of people with lower levels of education (RR: 1.02; 95% CI: 1.01 - 1.03), the proportion of labour workers (RR: 0.97; 95% CI: 0.95 - 1.00) and vegetation density (RR: 1.02; 95% CI: 1.00 - 1.04). However, RRV incidence for high risk areas (ie, SLAs with higher incidence of RRV) was significantly associated with mosquito density (RR: 1.01; 95% CI: 1.00 - 1.01), SOI at a lag of 3 months (RR: 1.48; 95% CI: 1.23 - 1.78), human population density (RR: 3.77; 95% CI: 1.35 - 10.51), the proportion of indigenous population (RR: 0.56; 95% CI: 0.37 - 0.87) and the proportion of overseas visitors (RR: 0.57; 95% CI: 0.35 - 0.92). It is acknowledged that some of these risk factors, while statistically significant, are small in magnitude. However, given the high incidence of RRV, they may still be important in practice. The results of this study suggest that the spatial pattern of RRV disease in Brisbane is determined by a combination of ecological, socio-economic and environmental factors. The possibility of developing an epidemic forecasting system for RRV disease was explored using the multivariate Seasonal Auto-regressive Integrated Moving Average (SARIMA) technique. The results of this study suggest that climatic variability, particularly precipitation, may have played a significant role in the transmission of RRV disease in Brisbane. This finding cannot entirely be explained by confounding factors such as other socio-ecological conditions because they have been unlikely to change dramatically on a monthly time scale in this city over the past two decades. SARIMA models show that monthly precipitation at a lag 2 months (=0.004,p=0.031) was statistically significantly associated with RRV disease. It suggests that there may be 50 more cases a year for an increase of 100 mm precipitation on average in Brisbane. The predictive values in the model were generally consistent with actual values (root-mean-square error (RMSE): 1.96). Therefore, this model may have applications as a decision support tool in disease control and risk-management planning programs in Brisbane. The Polynomial distributed lag (PDL) time series regression models were performed to examine the associations between rainfall, mosquito density and the occurrence of RRV after adjusting for season and auto-correlation. The PDL model was used because rainfall and mosquito density can affect not merely RRV occurring in the same month, but in several subsequent months. The rationale for the use of the PDL technique is that it increases the precision of the estimates. We developed an epidemic forecasting model to predict incidence of RRV disease. The results show that 95% and 85% of the variation in the RRV disease was accounted for by the mosquito density and rainfall, respectively. The predictive values in the model were generally consistent with actual values (RMSE: 1.25). The model diagnosis reveals that the residuals were randomly distributed with no significant auto-correlation. The results of this study suggest that PDL models may be better than SARIMA models (R-square increased and RMSE decreased). The findings of this study may facilitate the development of early warning systems for the control and prevention of this widespread disease. Further analyses were conducted using classification trees to identify major mosquito species of Ross River virus (RRV) transmission and explore the threshold of mosquito density for RRV disease in Brisbane, Australia. The results show that Ochlerotatus vigilax (RR: 1.028; 95% CI: 1.001 - 1.057) and Culex annulirostris (RR: 1.013, 95% CI: 1.003 - 1.023) were significantly associated with RRV disease cycles at a lag of 1 month. The presence of RRV was associated with average monthly mosquito density of 72 Ochlerotatus vigilax and 52 Culex annulirostris per light trap. These results may also have applications as a decision support tool in disease control and risk management planning programs. As RRV has significant impact on population health, industry, and tourism, it is important to develop an epidemic forecast system for this disease. The results of this study show the disease surveillance data can be integrated with social, biological and environmental databases. These data can provide additional input into the development of epidemic forecasting models. These attempts may have significant implications in environmental health decision-making and practices, and may help health authorities determine public health priorities more wisely and use resources more effectively and efficiently.

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Kassim, H. "Experimental design criteria for parameter estimation and response prediction for inverse polynomial regression models." Thesis, University of Reading, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.383442.

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Sundström, David. "On specification and inference in the econometrics of public procurement." Doctoral thesis, Umeå universitet, Nationalekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-121681.

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In Paper [I] we use data on Swedish public procurement auctions for internal regularcleaning service contracts to provide novel empirical evidence regarding green publicprocurement (GPP) and its effect on the potential suppliers’ decision to submit a bid andtheir probability of being qualified for supplier selection. We find only a weak effect onsupplier behavior which suggests that GPP does not live up to its political expectations.However, several environmental criteria appear to be associated with increased complexity,as indicated by the reduced probability of a bid being qualified in the postqualificationprocess. As such, GPP appears to have limited or no potential to function as an environmentalpolicy instrument. In Paper [II] the observation is made that empirical evaluations of the effect of policiestransmitted through public procurements on bid sizes are made using linear regressionsor by more involved non-linear structural models. The aspiration is typically to determinea marginal effect. Here, I compare marginal effects generated under both types ofspecifications. I study how a political initiative to make firms less environmentally damagingimplemented through public procurement influences Swedish firms’ behavior. Thecollected evidence brings about a statistically as well as economically significant effect onfirms’ bids and costs. Paper [III] embarks by noting that auction theory suggests that as the number of bidders(competition) increases, the sizes of the participants’ bids decrease. An issue in theempirical literature on auctions is which measurement(s) of competition to use. Utilizinga dataset on public procurements containing measurements on both the actual and potentialnumber of bidders I find that a workhorse model of public procurements is bestfitted to data using only actual bidders as measurement for competition. Acknowledgingthat all measurements of competition may be erroneous, I propose an instrumental variableestimator that (given my data) brings about a competition effect bounded by thosegenerated by specifications using the actual and potential number of bidders, respectively.Also, some asymptotic results are provided for non-linear least squares estimatorsobtained from a dependent variable transformation model. Paper [VI] introduces a novel method to measure bidders’ costs (valuations) in descending(ascending) auctions. Based on two bounded rationality constraints bidders’costs (valuations) are given an imperfect measurements interpretation robust to behavioraldeviations from traditional rationality assumptions. Theory provides no guidanceas to the shape of the cost (valuation) distributions while empirical evidence suggeststhem to be positively skew. Consequently, a flexible distribution is employed in an imperfectmeasurements framework. An illustration of the proposed method on Swedishpublic procurement data is provided along with a comparison to a traditional BayesianNash Equilibrium approach.

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Gray, C. M. "Use of the Bayesian family of methods to correct for effects of exposure measurement error in polynomial regression models." Thesis, London School of Hygiene and Tropical Medicine (University of London), 2018. http://researchonline.lshtm.ac.uk/4649757/.

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Measurement error in a continuous exposure, if ignored, may cause bias in the estimation of the relationship between exposure and outcome. This presents a significant challenge for understanding exposure-outcome associations in many areas of research, including economic, social, medical and epidemiological research. The presence of classical, i.e. random, measurement error in a continuous exposure has been shown to lead to underestimation of a simple linear relationship. When the functional form of the exposure within a regression model is not linear, i.e. when transformations of the exposure are included, measurement error obscures the true shape of the relationship by making the association appear more linear. Bias in this case will be unknown in direction and vary by exposure level. The most commonly used method for measurement error correction is regression calibration, but this requires an approximation for logistic and survival regression models and does not extend easily to more complex error models. This work investigates three methods for measurement error correction from the Bayesian family of methods: Bayesian analysis using Markov chain Monte Carlo (MCMC), integrated nested Laplace approximations (INLA), and multiple imputation (MI). These have been proposed for measurement error correction but have not been extensively compared, extended for use in several important scenarios, or applied to flexible parametric models. The focus on Bayesian methods was motivated by their flexibility to accommodate complex measurement error models and non-linear exposure-outcome associations. Polynomial regression models are widely used and are often the most interpretable models. In order for measurement error correction methods to be widely implemented, they should be able to accommodate known polynomial transformations as well as model selection procedures when the functional form of the error-prone exposure is unknown. Therefore, in this thesis, correction methods are integrated with the fractional polynomial method, a flexible polynomial model-building procedure for positive continuous variables. In this thesis, I perform a large simulation study comparing proposed methods for measurement error correction from the Bayesian family (i.e. MCMC, INLA, and MI) to the most common method of measurement error correction. Extensions of INLA and MI are presented in order to accommodate both a validation study setting wherein the error-free exposure is measured in a subgroup as well as a replicate study setting wherein there are multiple measures of the error-prone exposure. In order to accommodate unknown polynomial transformations of the error-prone variable, two approaches not used before in this context are proposed and explored in simulation studies alongside more standard methods. The first approach uses Bayesian posterior means in lieu of maximum likelihood estimates within regression calibration. The second approach adapts methods of Bayesian variable selection to the selection of the best polynomial transformation of the error-prone exposure while accommodating measurement error. Successful methods are applied to a motivating example, fitting the non-linear association between alcohol intake and all-cause mortality. By combining measurement error correction adaptable to complex error models with polynomial regression inclusive of model-selection, this work fills a niche which will facilitate wider use of measurement error correction techniques.

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Books on the topic "Polynomial regression model"

1

Willi, Sauerbrei, ed. Multivariable model-building: A pragmatic approach to regression analysis based on fractional polynomials for continuous variables. John Wiley, 2008.

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Royston, Patrick, and Willi Sauerbrei. Multivariable Model - Building: A Pragmatic Approach to Regression Anaylsis Based on Fractional Polynomials for Modelling Continuous Variables. Wiley & Sons, Incorporated, John, 2008.

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Royston, Patrick, and Willi Sauerbrei. Multivariable Model - Building: A Pragmatic Approach to Regression Anaylsis Based on Fractional Polynomials for Modelling Continuous Variables. Wiley & Sons, Incorporated, John, 2008.

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Book chapters on the topic "Polynomial regression model"

1

Gupta, B. B., P. K. Agrawal, A. Mishra, and M. K. Pattanshetti. "On Estimating Strength of a DDoS Attack Using Polynomial Regression Model." In Advances in Computing and Communications. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22726-4_26.

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Rumantir, Grace W., and Chris S. Wallace. "Sampling of Highly Correlated Data for Polynomial Regression and Model Discovery." In Advances in Intelligent Data Analysis. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44816-0_37.

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Brown, Jonathon D. "Polynomial Regression." In Linear Models in Matrix Form. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_10.

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Westfall, Peter H., and Andrea L. Arias. "Polynomial Models and Interaction (Moderator) Analysis." In Understanding Regression Analysis. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781003025764-9.

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Diestmann, Thomas, Nils Broedling, Benedict Götz, and Tobias Melz. "Surrogate Model-Based Uncertainty Quantification for a Helical Gear Pair." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_16.

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AbstractCompetitive industrial transmission systems must perform most efficiently with reference to complex requirements and conflicting key performance indicators. This design challenge translates into a high-dimensional multi-objective optimization problem that requires complex algorithms and evaluation of computationally expensive simulations to predict physical system behavior and design robustness. Crucial for the design decision-making process is the characterization, ranking, and quantification of relevant sources of uncertainties. However, due to the strict time limits of product development loops, the overall computational burden of uncertainty quantification (UQ) may even drive state-of-the-art parallel computing resources to their limits. Efficient machine learning (ML) tools and techniques emphasizing high-fidelity simulation data-driven training will play a fundamental role in enabling UQ in the early-stage development phase.This investigation surveys UQ methods with a focus on noise, vibration, and harshness (NVH) characteristics of transmission systems. Quasi-static 3D contact dynamic simulations are performed to evaluate the static transmission error (TE) of meshing gear pairs under different loading and boundary conditions. TE indicates NVH excitation and is typically used as an objective function in the early-stage design process. The limited system size allows large-scale design of experiments (DoE) and enables numerical studies of various UQ sampling and modeling techniques where the design parameters are treated as random variables associated with tolerances from manufacturing and assembly processes. The model accuracy of generalized polynomial chaos expansion (gPC) and Gaussian process regression (GPR) is evaluated and compared. The results of the methods are discussed to conclude efficient and scalable solution procedures for robust design optimization.

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Seal, Victor, Arnab Raha, Shovan Maity, Souvik Kr Mitra, Amitava Mukherjee, and Mrinal Kanti Naskar. "A Real Time Multivariate Robust Regression Based Flood Prediction Model Using Polynomial Approximation for Wireless Sensor Network Based Flood Forecasting Systems." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27317-9_44.

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Dikici, Engin, and Fredrik Orderud. "Polynomial Regression Based Edge Filtering for Left Ventricle Tracking in 3D Echocardiography." In Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28326-0_17.

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"Polynomial Model Estimation." In Regression Modeling. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420091984-c16.

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"Polynomial Model Estimation." In Regression Modeling. Chapman and Hall/CRC, 2009. http://dx.doi.org/10.1201/9781420091984-19.

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Bukovsky, Ivo, Peter M. Benes, and Martin Vesely. "Introduction and Application Aspects of Machine Learning for Model Reference Adaptive Control With Polynomial Neurons." In Artificial Intelligence and Machine Learning Applications in Civil, Mechanical, and Industrial Engineering. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0301-0.ch004.

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This chapter recalls the nonlinear polynomial neurons and their incremental and batch learning algorithms for both plant identification and neuro-controller adaptation. Authors explain and demonstrate the use of feed-forward as well as recurrent polynomial neurons for system approximation and control via fundamental, though for practice efficient machine learning algorithms such as Ridge Regression, Levenberg-Marquardt, and Conjugate Gradients, authors also discuss the use of novel optimizers such as ADAM and BFGS. Incremental gradient descent and RLS algorithms for plant identification and control are explained and demonstrated. Also, novel BIBS stability for recurrent HONUs and for closed control loops with linear plant and nonlinear (HONU) controller is discussed and demonstrated.

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Conference papers on the topic "Polynomial regression model"

1

Nowosielski, Artur, Piotr Andrzej Kowalski, and Piotr Kulczycki. "A Database Performance Polynomial Multiple Regression Model." In 2017 Federated Conference on Computer Science and Information Systems. IEEE, 2017. http://dx.doi.org/10.15439/2017f416.

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Hu, Yu, Renhua Wang, and Lu Sun. "Polynomial regression model for duration prediction in Mandarin." In Interspeech 2004. ISCA, 2004. http://dx.doi.org/10.21437/interspeech.2004-292.

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Qi, Peiyan, Zheng Tian, and Xifa Duan. "Sequential monitoring change in persistence of polynomial regression model." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234076.

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Zhu, Houkun, Yuan Luo, Chuliang Weng, and Minglu Li. "A Collaborative Filtering Recommendation Model Using Polynomial Regression Approach." In 2009 Fourth ChinaGrid Annual Conference (ChinaGrid). IEEE, 2009. http://dx.doi.org/10.1109/chinagrid.2009.34.

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Li, Hongran, and Shigeru Yamamoto. "Polynomial regression based model-free predictive control for nonlinear systems." In 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE). IEEE, 2016. http://dx.doi.org/10.1109/sice.2016.7749264.

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Li, Hongran, and Shigeru Yamamoto. "A model-free predictive control method based on polynomial regression." In 2016 SICE International Symposium on Control Systems (ISCS). IEEE, 2016. http://dx.doi.org/10.1109/siceiscs.2016.7470167.

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Wu, Junhong, Zhenyu Li, and Shengtong Yang. "COVID-19 Dynamics Prediction by Improved Multi-Polynomial Regression Model." In CONF-CDS 2021: The 2nd International Conference on Computing and Data Science. ACM, 2021. http://dx.doi.org/10.1145/3448734.3450847.

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Silva, Anderson A. A., Elvis Pontes, Fen Zhou, and Sergio Takeo Kofuji. "Grey model and polynomial regression for identifying malicious nodes in MANETs." In GLOBECOM 2014 - 2014 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7036801.

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Green, David, and Filip Rindler. "MODEL INFERENCE FOR ORDINARY DIFFERENTIAL EQUATIONS BY PARAMETRIC POLYNOMIAL KERNEL REGRESSION." In 2nd International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2019. http://dx.doi.org/10.7712/120219.6340.18533.

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Palenichka, Roman M., and Iryna B. Ivasenko. "Fast and robust parameter estimation in the polynomial regression model of images." In Electronic Imaging '99, edited by Edward R. Dougherty and Jaakko T. Astola. SPIE, 1999. http://dx.doi.org/10.1117/12.341096.

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