Relevant bibliographies by topics / Multiple linear regression

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Multiple linear regression'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Multiple linear regression"

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Genç, S., and M. Mendeş. "Multiple Linear Regression versus Automatic Linear Modelling." Arquivo Brasileiro de Medicina Veterinária e Zootecnia 76, no. 1 (2024): 131–36. http://dx.doi.org/10.1590/1678-4162-13071.

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ABSTRACT In this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose. Random numbers generated from multivariate normal distribution by using RNMVN function of IMSL library of Microsoft FORTRAN Developer Studio composed the material of this study. Results of the simulation study showed that the sample size and the number of predictors are the main factors that lead to produce different results. Although both methods gave ve
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Krzywinski, Martin, and Naomi Altman. "Multiple linear regression." Nature Methods 12, no. 12 (2015): 1103–4. http://dx.doi.org/10.1038/nmeth.3665.

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Slinker, Bryan K., and Stanton A. Glantz. "Multiple Linear Regression." Circulation 117, no. 13 (2008): 1732–37. http://dx.doi.org/10.1161/circulationaha.106.654376.

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Fletcher, J. "Multiple linear regression." BMJ 338, jan28 3 (2009): b167. http://dx.doi.org/10.1136/bmj.b167.

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Bangdiwala, Shrikant I. "Regression: multiple linear." International Journal of Injury Control and Safety Promotion 25, no. 2 (2018): 232–36. http://dx.doi.org/10.1080/17457300.2018.1452336.

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Grégoire, G. "Multiple Linear Regression." EAS Publications Series 66 (2014): 45–72. http://dx.doi.org/10.1051/eas/1466005.

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Narula, Subhash C., and John F. Wellington. "Multiple criteria linear regression." European Journal of Operational Research 181, no. 2 (2007): 767–72. http://dx.doi.org/10.1016/j.ejor.2006.06.026.

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Pandis, Nikolaos. "Multiple linear regression analysis." American Journal of Orthodontics and Dentofacial Orthopedics 149, no. 4 (2016): 581. http://dx.doi.org/10.1016/j.ajodo.2016.01.012.

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Marill, Keith A. "Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression." Academic Emergency Medicine 11, no. 1 (2004): 94–102. http://dx.doi.org/10.1111/j.1553-2712.2004.tb01379.x.

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Smith, D. M., and M. G. Kenward. "Extensions of multiple linear regression." Communications in Statistics - Theory and Methods 29, no. 9-10 (2000): 2033–53. http://dx.doi.org/10.1080/03610920008832594.

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Dissertations / Theses on the topic "Multiple linear regression"

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Ollikainen, Kati. "PARAMETER ESTIMATION IN LINEAR REGRESSION." Doctoral diss., University of Central Florida, 2006. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4138.

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Today increasing amounts of data are available for analysis purposes and often times for resource allocation. One method for analysis is linear regression which utilizes the least squares estimation technique to estimate a model's parameters. This research investigated, from a user's perspective, the ability of linear regression to estimate the parameters' confidence intervals at the usual 95% level for medium sized data sets. A controlled environment using simulation with known data characteristics (clean data, bias and or multicollinearity present) was used to show underlying problems exist
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Forslund, Gustaf, and David Åkesson. "Predicting share price by using Multiple Linear Regression." Thesis, KTH, Farkost och flyg, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140645.

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The aim of the project was to design a multiple linear regression model and use it to predict the share’s closing price for 44 companies listed on the OMX Stockholm stock exchange’s Large Cap list. The model is intended to be used as a day trading guideline i.e. today’s information is used to predict tomorrow’s closing price. The regression was done in Microsoft Excel 2010[18] by using its built-in function LINEST. The LINEST-function uses the dependent variable y and all the covariates x to calculate the β-value belonging to each covariate. Several multiple linear regression models were creat
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Saleem, Aban, and Jacob Blomgren. "Modelling Pupils’ Grades with Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-275672.

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This thesis was based on the subjects of mathematical statistics and industrial economics and management in order to analyze the grades of pupils in the final year of elementary school. The purpose was to find out what variables had a statistically significant impact on pupils’ final grades so that municipalities and schools could better understand what variables are important when trying to improve the average school results. A multiple regression model was used on data, obtained from the database of Skolverket, in order to examine what variables were statistically important. The final regres
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Gustafsson, Alexander, and Sebastian Wogenius. "Modelling Apartment Prices with the Multiple Linear Regression Model." Thesis, KTH, Matematisk statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-146735.

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This thesis examines factors that are of most statistical significance for the sales prices of apartments in the Stockholm City Centre. Factors examined are address, area, balcony, construction year, elevator, fireplace, floor number, maisonette, monthly fee, penthouse and number of rooms. On the basis of this examination, a model for predicting prices of apartments is constructed. In order to evaluate how the factors influence the price, this thesis analyses sales statistics and the mathematical method used is the multiple linear regression model. In a minor case-study and literature review,
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Peraça, Maria da Graça Teixeira. "Modelos para estimativa do grau de saturação do concreto mediante variáveis ambientais que influenciam na sua variação." reponame:Repositório Institucional da FURG, 2009. http://repositorio.furg.br/handle/1/3436.

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Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Engenharia Oceânica, Escola de Engenharia, 2009.<br>Submitted by Lilian M. Silva (lilianmadeirasilva@hotmail.com) on 2013-04-22T19:51:54Z No. of bitstreams: 1 Modelos para estimativa do Grau de Saturação do concreto mediante Variáveis Ambientais que influenciam na sua variação.pdf: 2786682 bytes, checksum: df174dab02a19756db94fc47c6bb021d (MD5)<br>Approved for entry into archive by Bruna Vieira(bruninha_vieira@ibest.com.br) on 2013-06-03T19:20:55Z (GMT) No. of bitstreams: 1 Modelos para estimativa do Grau
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Alt, Raimund. "Multiple hypotheses testing in the linear regression model with applications to economics and finance /." Göttingen : Cuvillier, 2005. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=013081924&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Kinns, David Jonathan. "Multiple case influence analysis with particular reference to the linear model." Thesis, University of Birmingham, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.368427.

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Basha, Elizabeth (Elizabeth Ann). "In-situ prediction on sensor networks using distributed multiple linear regression models." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/60096.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student submitted PDF version of thesis.<br>Includes bibliographical references (p. 199-208).<br>Within sensor networks for environmental monitoring, a class of problems exists that requires in-situ control and modeling. In this thesis, we provide a solution to these problems, enabling model-driven computation wher
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Galijasevic, Amar, and Josef Tegbaru. "Can IPO first day returns be predicted? A multiple linear regression analysis." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254293.

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During the last three years the Swedish stock market has showed a strong upwards movement from the lows of 2016. At the same time the IPO activity has been large and a lot of the offerings have had a positive return during the first day of trading in the market. The goal of this study is to analyze if there is any particular IPO specific data that has a correlation with the first day return and if it can be used to predict the first day return for future IPO’s. If any regressors were shown to have correlation with the first day return, the goal is also to find a subset of regressors with even
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Leda, Victor Costa [UNESP]. "Modelagem da produtividade de cana-de-açúcar utilizando índices de vegetação." Universidade Estadual Paulista (UNESP), 2016. http://hdl.handle.net/11449/143945.

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Submitted by VICTOR COSTA LEDA null (victorleda@gmail.com) on 2016-09-20T13:48:09Z No. of bitstreams: 1 Dissertação Victor Costa Leda Final.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5)<br>Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-09-22T19:30:58Z (GMT) No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5)<br>Made available in DSpace on 2016-09-22T19:30:58Z (GMT). No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5)
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Books on the topic "Multiple linear regression"

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Karim, Samsul Ariffin Abdul, and Nur Fatonah Kamsani. Water Quality Index Prediction Using Multiple Linear Fuzzy Regression Model. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3485-0.

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Shelton, Katherine Lesley. An illustration of heteroscedasticity in the multiple linear regression model. The author], 1985.

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J, Niccolucci Michael, Schuster Ervin G, and Intermountain Research Station (Ogden, Utah), eds. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

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Booth, Gordon D. Identifying proxy sets in multiple linear regression: An aid to better coefficient interpretation. U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1994.

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Center, Lewis Research, ed. A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys. National Aeronautics and Space Administration, Lewis Research Center, 1985.

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Zorn, Troy G. Utility of species-specific, multiple linear regression models for prediction of fish assemblages in rivers of Michigan's lower peninsula. Michigan Dept. of Natural Resources, Fisheries Division, 2004.

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Scott Jones, Julie. Learn to Clean and Prepare Data for a Multiple Linear Regression in SPSS Using Data From the Health Survey for England (2011) (Teaching Dataset). SAGE Publications, Ltd., 2022. http://dx.doi.org/10.4135/9781529605327.

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Multiple Linear Regression. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630138.

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Roback, Paul, and Julie Legler. Beyond Multiple Linear Regression. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9780429066665.

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Creating Multiple Linear Regression Model. Starttech Educational Services LLP, 2020. http://dx.doi.org/10.4135/9781529630251.

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Book chapters on the topic "Multiple linear regression"

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Olive, David J. "Multiple Linear Regression." In Linear Regression. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55252-1_2.

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Brown, Jonathon D. "Multiple Regression." In Linear Models in Matrix Form. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11734-8_4.

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Hoffmann, John P. "Multiple Linear Regression Models." In Linear Regression Models. Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003162230-4.

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Eberly, Lynn E. "Multiple Linear Regression." In Topics in Biostatistics. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-530-5_9.

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Flury, Bernhard, and Hans Riedwyl. "Multiple linear regression." In Multivariate Statistics. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-1217-5_5.

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Armstrong, Richard A., and Anthony C. Hilton. "Multiple Linear Regression." In Statistical Analysis in Microbiology: Statnotes. John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9780470905173.ch25.

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Holmes, William H., and William C. Rinaman. "Multiple Linear Regression." In Statistical Literacy for Clinical Practitioners. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-12550-3_14.

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Sheather, Simon J. "Multiple Linear Regression." In Springer Texts in Statistics. Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-09608-7_5.

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Gooch, Jan W. "Multiple Linear Regression." In Encyclopedic Dictionary of Polymers. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_15294.

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Everitt, Brian, and Sophia Rabe-Hesketh. "Multiple Linear Regression." In Analyzing Medical Data Using S-PLUS. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3285-6_9.

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Conference papers on the topic "Multiple linear regression"

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Zhou, Xiwei, Zongyang Sun, and Sai Liu. "PCB Pre-layout Judgment Method Based on Multiple Linear Regression Model." In 2025 7th International Conference on Information Science, Electrical and Automation Engineering (ISEAE). IEEE, 2025. https://doi.org/10.1109/iseae64934.2025.11042103.

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Ivanovski, Tomislav, Guoxiang Zhang, Tomislav Jemric, Marko Gulic, and Maja Matetic. "Fruit firmness prediction using multiple linear regression." In 2020 43rd International Convention on Information, Communication and Electronic Technology (MIPRO). IEEE, 2020. http://dx.doi.org/10.23919/mipro48935.2020.9245213.

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Castillo-Garit, Juan, Yudith Cañizares-Carmenate, Karel Mena-Ulecia, Yunier Perera-Sardiña, and Francisco Torrens. "Multiple Linear Regression Model of Thermolysin Inhibitors." In MOL2NET 2016, International Conference on Multidisciplinary Sciences, 2nd edition. MDPI, 2017. http://dx.doi.org/10.3390/mol2net-02-03872.

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R, Madhumathi, Arumuganathan T, Shruthi R, and Raghavendar S. "Soil NPK Prediction Using Multiple Linear Regression." In 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE, 2022. http://dx.doi.org/10.1109/icaccs54159.2022.9785338.

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Chuan, Zun Liang, David Chong Teak Wei, Adam Shariff Bin Adli Aminuddin, Soo-Fen Fam, and Tan Lit Ken. "Comparison of multiple linear regression and multiple nonlinear regression models for predicting rice production." In 5TH INTERNATIONAL CONFERENCE ON MATHEMATICAL SCIENCES (ICMS5). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0227872.

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Guo, Yuanchun. "Unbiased Least Squares Regression Coefficients for Multiple Linear Regression Mathematical Models." In ICISCAE 2021: 2021 IEEE 4th International Conference on Information Systems and Computer Aided Education. ACM, 2021. http://dx.doi.org/10.1145/3482632.3484099.

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Xu, Lei, Hongliang Yu, Xiaohong Wang, and Guochuan Sun. "Application of multiple linear regression in cement burden." In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2019. http://dx.doi.org/10.1109/imcec46724.2019.8984127.

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Gomis, Francois Kassene, Mamadou Samba Camara, Idy Diop, Sidi Mohamed Farssi, Khaly Tall, and Birahime Diouf. "Multiple linear regression for universal steganalysis of images." In 2018 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2018. http://dx.doi.org/10.1109/isacv.2018.8354060.

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Aiyin, Wang, and Xu Yanmei. "Multiple Linear Regression Analysis of Real Estate Price." In 2018 International Conference on Robots & Intelligent System (ICRIS). IEEE, 2018. http://dx.doi.org/10.1109/icris.2018.00145.

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Audone, Bruno, and Gergio Giunta. "Multiple Linear Regression to detect shielding effectiveness degradations." In 2008 International Symposium on Electromagnetic Compatibility - EMC Europe. IEEE, 2008. http://dx.doi.org/10.1109/emceurope.2008.4786802.

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Reports on the topic "Multiple linear regression"

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Kubik, Harold. MLRP, Multiple Linear Regression Program. Defense Technical Information Center, 1986. http://dx.doi.org/10.21236/ada204565.

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Cook, Steve, Peter Dawson, and Duncan Watson. Building multiple linear regression skills via ‘puzzling’ active learning. The Economics Network, 2024. http://dx.doi.org/10.53593/n4163a.

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Meloncelli, Daniel. Regression Analysis using R. Instats Inc., 2025. https://doi.org/10.61700/j3s1r521de2ee1472.

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This seminar provides a comprehensive introduction to regression analysis using R. Participants will explore the fundamentals of correlation, simple linear regression, multiple regression, and logistic regression. The seminar emphasises practical application, guiding attendees through data preparation, model fitting, assumption checking, and interpretation of results. Hands-on sessions will enable participants to apply regression techniques to real-world datasets, enhancing their analytical skills for research purposes.
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Collins, Abigail. Predicting Student Success at a Large State University using Multiple Linear Regression and Hierarchical Clustering. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1143.

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Chernis, Tony, Niko Hauzenberger, Florian Huber, Gary Koop, and James Mitchell. Predictive Density Combination Using a Tree-Based Synthesis Function. Federal Reserve Bank of Cleveland, 2023. http://dx.doi.org/10.26509/frbc-wp-202330.

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Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. The key ingredient in BPS is a “synthesis” function. This is typically specified parametrically as a dynamic linear regression. In this paper, we develop a nonparametric treatment of the synthesis function using regression trees. We show the advantages of our tree-based approach in two macroeconomic forecasting applications. The first uses density forecasts for GDP growth from th
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Ruangpornvisuti, Vithaya. A Study of conformational equilibrium of semicarbazone derivatives and their complexes with cations : research report. Chulalongkorn University, 2006. https://doi.org/10.58837/chula.res.2006.36.

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The structure optimizations of picolinaldehyde N-oxide thiosemicarbazone (Hpiotsc), 2-benzoylpyridine semicarbazone (H2BzPS), their imino tautomers and their complexes with Ni(II), Cu(II) and Zn(II) were carried out using DFT calculations at the B3LYP/LANL2DZ level of theory. Thermodynamic properties of tautomerizations of Hpiotsc and H2BzPS and complexations of their complexes derived from the frequency calculations at the same level were obtained. The B3LYP/LANL2DZ-optimized geometry parameters for the complexes of [[Ni(Hpiotsc)[subscript 2]][superscript 2+]], [Cu(Hpiotsc).Cl[subscript 2]] a
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Huang, Tao, and Venkatesh Merwade. Developing Customized NRCS Unit Hydrographs (Finley UHs) for Ungauged Watersheds in Indiana. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317644.

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The Natural Resources Conservation Service (NRCS, formerly the Soil Conservation Service, SCS) unit hydrograph (UH) is one of the most commonly used synthetic UH methods for hydrologic modeling and engineering design all over the world. However, previous studies have shown that the application of the NRCS UH method for some ungauged watersheds in the state of Indiana produced unrealistic flood predictions for both the peak discharge and the time to peak. The objective of this work is to customize the NRCS UH by analyzing the role of its two key parameters, namely, the peak rate factor (PRF) an
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Straub, Stéphane. Empirical Determinants of Good Institutions: Do We Know Anything? Inter-American Development Bank, 2000. http://dx.doi.org/10.18235/0011558.

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Abundant empirical evidence links well-functioning institutions and good governance to better economic and social outcomes. It is thus an important challenge to determine which conjunction of factors produces better institutions. Along this line, the objective of this paper is twofold. First, it examines the existing results of the literature on this matter in a critical way, tries to assess their robustness, and explores alternative methodology. Second, it makes use of a more comprehensive database, including all the aspects previously analyzed in a separate manner, to derive systematic empir
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Owen, Patrick J., Shane A. Kavanagh, and Anthony D. LaMontagne. What can be done to Improve the Mental Health of University Students? Identifying Determinants of Psychological Distress at an Australian University. Journal of the Australian and New Zealand Student Services Association, 2025. https://doi.org/10.30688/janzssa.2025-1-03.

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Background: Mental health problems are common among university students. This study examined associations between determinants of mental health and psychological distress among university students with the aim of identifying priorities for policy and practice action. Methods: This cross-sectional study included 3,581 university students (response rate: 7.1%) who completed an anonymous online survey. Individual determinants included socio-demographics and financial, housing, and employment status. Community (university) determinants were social inclusion, social acceptance, and overall satisfac
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Alexander, Serena E., Mariela Alfonzo, and Kevin Lee. Safeguarding Equity in Off-Site Vehicle Miles Traveled (VMT) Mitigation in California. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2027.

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Historically, the State of California assessed the environmental impacts of proposed developments based on how it was projected to affect an area’s level of service (LOS). However, as LOS focused on traffic delays, many agencies simply widened roads, which was an ineffective way to reduce greenhouse gas emissions (GHGs). With the passage of Senate Bill (SB)743 in 2013, LOS was replaced by Vehicle Miles Traveled (VMT) as a more appropriate metric by which to gauge the environmental impacts of proposed development. Additionally, SB 743 presented an opportunity for off-site VMT mitigation strateg
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