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George E. P. Box

(deceased)

Personal Details

This person is deceased (Date: 28 Mar 2013)
First Name:George
Middle Name:E. P.
Last Name:Box
Suffix:
RePEc Short-ID:pbo691
http://en.wikipedia.org/wiki/George_E._P._Box

Research output

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Jump to: Working papers Articles Chapters

Working papers

  1. George E. P. Box & David A. Pierce, 1981. "Estimating current trend and growth rates in seasonal time series," Special Studies Papers 156, Board of Governors of the Federal Reserve System (U.S.).

Articles

  1. George Box & Alberto Luceno, 2002. "Feedforward as a supplement to feedback adjustment in allowing for feedstock changes," Journal of Applied Statistics, Taylor & Francis Journals, vol. 29(8), pages 1241-1254.
  2. George Box, 2001. "Statistics for discovery," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 285-299.
  3. George Box & Ian Hau, 2001. "Experimental designs when there are one or more factor constraints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(8), pages 973-989.
  4. Alberto Luceno George & E. P. Box, 2000. "Influence of the sampling interval, decision limit and autocorrelation on the average run length in Cusum charts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(2), pages 177-183.
  5. Nozer Singpurwalla & G. Box & D. Cox & D. Dey & A. Fries & J. Ghosh & M. Gómez-Villegas & T. Irony & W. Kliemann & S. Kotz & D. Lindley & M. McGrath & D. Peña & N. Singpurwalla, 1998. "The stochastic control of process capability indices," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 7(1), pages 1-74, June.
  6. George Box, 1994. "Statistics and Quality Improvement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(2), pages 209-229, March.
  7. Bovas Abraham & George E. P. Box, 1978. "Deterministic and Forecast‐Adaptive Time‐Dependent Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(2), pages 120-130, June.
  8. Bovas Abraham & George E. P. Box, 1978. "Linear Models and Spurious Observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(2), pages 131-138, June.
  9. J. Ledolter & G. Box, 1978. "Conditions for the optimality of exponential smoothing forecast procedures," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 25(1), pages 77-93, December.
  10. G. E. P. Box & G. C. Tiao, 1976. "Comparison of Forecast and Actuality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(3), pages 195-200, November.
  11. G. E. P. Box & G. M. Jenkins, 1968. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 17(2), pages 91-109, June.
  12. George E. P. Box, 1957. "Evolutionary Operation: A Method for Increasing Industrial Productivity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 6(2), pages 81-101, June.

Chapters

  1. George E. P. Box & Steven C. Hillmer & George C. Tiao, 1978. "Analysis and Modeling of Seasonal Time Series," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 309-344, National Bureau of Economic Research, Inc.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

    Sorry, no citations of working papers recorded.

Articles

  1. George Box, 2001. "Statistics for discovery," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 285-299.

    Cited by:

    1. Subhadeep & Mukhopadhyay, 2022. "Modelplasticity and Abductive Decision Making," Papers 2203.03040, arXiv.org, revised Mar 2023.
    2. Deep Mukhopadhyay, 2021. "Abductive Inference and C. S. Peirce: 150 Years Later," Papers 2111.08054, arXiv.org, revised Feb 2023.
    3. Subhadeep Mukhopadhyay, 2023. "Abductive Inference and C. S. Peirce: 150 Years Later," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 21(1), pages 123-149, March.
    4. Subhadeep Mukhopadhyay, 2023. "Modelplasticity and abductive decision making," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 255-276, June.
    5. Sharma Mithun & Sharma Shilpi, 2021. "Critical Evaluation into the practical utility of the Design of Experiments," Engineering Management in Production and Services, Sciendo, vol. 13(3), pages 50-65, September.

  2. Alberto Luceno George & E. P. Box, 2000. "Influence of the sampling interval, decision limit and autocorrelation on the average run length in Cusum charts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(2), pages 177-183.

    Cited by:

    1. Luceno, Alberto & Puig-Pey, Jaime, 2002. "An accurate algorithm to compute the run length probability distribution, and its convolutions, for a Cusum chart to control normal mean," Computational Statistics & Data Analysis, Elsevier, vol. 38(3), pages 249-261, January.

  3. George Box, 1994. "Statistics and Quality Improvement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(2), pages 209-229, March.

    Cited by:

    1. Hubbard, Raymond & Lindsay, R. Murray, 2013. "The significant difference paradigm promotes bad science," Journal of Business Research, Elsevier, vol. 66(9), pages 1393-1397.
    2. Vladimir B. Bokov, 2007. "Theoretic and empirical data‐inclusive process characterization," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(3), pages 735-758, July.
    3. George Box, 2001. "Statistics for discovery," Journal of Applied Statistics, Taylor & Francis Journals, vol. 28(3-4), pages 285-299.
    4. Hubbard, Raymond & Lindsay, R. Murray, 2013. "From significant difference to significant sameness: Proposing a paradigm shift in business research," Journal of Business Research, Elsevier, vol. 66(9), pages 1377-1388.

  4. Bovas Abraham & George E. P. Box, 1978. "Linear Models and Spurious Observations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(2), pages 131-138, June.

    Cited by:

    1. Tiao, George C., 1991. "Bayesian outliers functions for linear models," UC3M Working papers. Economics 5816, Universidad Carlos III de Madrid. Departamento de Economía.
    2. B. Abraham & W. Wei, 1984. "Inferences about the parameters of a time series model with changing variance," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 31(1), pages 183-194, December.
    3. Luca Sartore & Lu Chen & Valbona Bejleri, 2024. "Empirical Inferences Under Bayesian Framework to Identify Cellwise Outliers," Stats, MDPI, vol. 7(4), pages 1-15, October.
    4. Hans, Christopher M. & Peruggia, Mario & Wang, Junyan, 2023. "Empirical Bayes Model Averaging with Influential Observations: Tuning Zellner’s g Prior for Predictive Robustness," Econometrics and Statistics, Elsevier, vol. 27(C), pages 102-119.
    5. Justel, A., 1998. "Heterogeneity and model uncertainty in bayesian regression models," DES - Working Papers. Statistics and Econometrics. WS 6260, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Hamura, Yasuyuki & Irie, Kaoru & Sugasawa, Shonosuke, 2022. "Log-regularly varying scale mixture of normals for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 173(C).
    7. Guttman, Irwin, 1992. "A Bayesian look at diagnostics in the univariate linear model," UC3M Working papers. Economics 2831, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Justel, Ana & Sánchez, María Jesús, 1994. "Grupos atípicos en modelos econométricos," DES - Documentos de Trabajo. Estadística y Econometría. DS 10755, Universidad Carlos III de Madrid. Departamento de Estadística.

  5. G. E. P. Box & G. C. Tiao, 1976. "Comparison of Forecast and Actuality," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(3), pages 195-200, November.

    Cited by:

    1. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 53(4), pages 671-690.
    2. Wang, Shin-Huei & Vasilakis, Chrysovalantis, 2013. "Recursive predictive tests for structural change of long-memory ARFIMA processes with unknown break points," Economics Letters, Elsevier, vol. 118(2), pages 389-392.
    3. Fan, Jianqing & Ke, Yuan & Wang, Kaizheng, 2020. "Factor-adjusted regularized model selection," Journal of Econometrics, Elsevier, vol. 216(1), pages 71-85.
    4. Alvarez, Luis J. & Delrieu, Juan C. & Jareño, Javier, 1997. "Restricted forecasts and economic target monitoring: An application to the Spanish Consumer Price Index," Journal of Policy Modeling, Elsevier, vol. 19(3), pages 333-349, June.
    5. Fiammetta Menchetti & Fabrizio Cipollini & Fabrizia Mealli, 2021. "Causal effect of regulated Bitcoin futures on volatility and volume," Papers 2109.15052, arXiv.org.
    6. Fiammetta Menchetti & Fabrizio Cipollini & Fabrizia Mealli, 2021. "Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach," Papers 2103.06740, arXiv.org, revised Sep 2021.
    7. Gonzalez, Pilar & Moral, Paz, 1995. "An analysis of the international tourism demand in Spain," International Journal of Forecasting, Elsevier, vol. 11(2), pages 233-251, June.
    8. Ali Akarca & Dimitri Andrianacos, 1997. "Detecting break in oil price series using the Box-Tiao method," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 3(2), pages 217-224, May.
    9. Victor Guerrero, 2005. "Restricted estimation of an adjusted time series: application to Mexico's industrial production index," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(2), pages 157-177.
    10. Holt, Matthew T. & Brandt, Jon A., 1985. "Forecasting Hog Prices Using Time Series Analysis Of Residuals," 1985 Annual Meeting, August 4-7, Ames, Iowa 278558, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    11. Victor Guerrero & Edmundo Berumen, 1998. "Forecasting electricity consumption with extra-model information provided by consumers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(2), pages 283-299.
    12. Ali T. Akarca, 2014. "How Should We Interpret the Outcome of the June 2015 Parliamentary Election in Turkey?," Ekonomi-tek - International Economics Journal, Turkish Economic Association, vol. 3(3), pages 1-22, September.
    13. McCrae, Michael, et al, 2002. "Can Cointegration-Based Forecasting Outperform Univariate Models? An Application to Asian Exchange Rates," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 21(5), pages 355-380, August.
    14. Kraay, Aart & Monokroussos, George, 1999. "Growth forecasts using time series and growth models," Policy Research Working Paper Series 2224, The World Bank.
    15. Ali Akarca & Dimitri Andrianacos, 1998. "The relationship between crude oil and gasoline prices," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 4(3), pages 282-288, August.

  6. G. E. P. Box & G. M. Jenkins, 1968. "Some Recent Advances in Forecasting and Control," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 17(2), pages 91-109, June.

    Cited by:

    1. Brano Glumac & Francois Des Rosiers, 2018. "Real estate and land property automated valuations systems: a taxonomy and conceptual model," ERES eres2018_148, European Real Estate Society (ERES).
    2. Liu, Xiao & Hu, Qunpeng & Li, Jinsong & Li, Weimin & Liu, Tong & Xin, Mingjun & Jin, Qun, 2024. "Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series," Applied Energy, Elsevier, vol. 367(C).
    3. Fieger, Peter & Rice, John, 2016. "Modelling Chinese Inbound Tourism Arrivals into Christchurch," MPRA Paper 75468, University Library of Munich, Germany.
    4. Xia Pan & Jeffrey Jarrett, 2012. "Why and how to use vector autoregressive models for quality control: the guideline and procedures," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(3), pages 935-948, April.
    5. Ralf Barkemeyer & Philippe Givry & Frank Figge, 2018. "Trends and patterns in sustainability-related media coverage: A classification of issue-level attention," Environment and Planning C, , vol. 36(5), pages 937-962, August.
    6. Jayesh Thaker & Robert Höller, 2022. "A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification," Energies, MDPI, vol. 15(8), pages 1-26, April.
    7. Amirhossein Sohrabbeig & Omid Ardakanian & Petr Musilek, 2023. "Decompose and Conquer: Time Series Forecasting with Multiseasonal Trend Decomposition Using Loess," Forecasting, MDPI, vol. 5(4), pages 1-13, December.
    8. Salvatore Carta & Andrea Medda & Alessio Pili & Diego Reforgiato Recupero & Roberto Saia, 2018. "Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data," Future Internet, MDPI, vol. 11(1), pages 1-19, December.
    9. Christian Sonesson, 2003. "Evaluations of some Exponentially Weighted Moving Average methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1115-1133.
    10. Hess, Alexander & Spinler, Stefan & Winkenbach, Matthias, 2021. "Real-time demand forecasting for an urban delivery platform," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    11. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    12. Jeon, Yunho & Seong, Sihyeon, 2022. "Robust recurrent network model for intermittent time-series forecasting," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1415-1425.
    13. Dat Thanh Tran & Alexandros Iosifidis & Juho Kanniainen & Moncef Gabbouj, 2017. "Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis," Papers 1712.00975, arXiv.org.
    14. Phinikarides, Alexander & Kindyni, Nitsa & Makrides, George & Georghiou, George E., 2014. "Review of photovoltaic degradation rate methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 143-152.
    15. Xing Wang & Yijun Wang & Bin Weng & Aleksandr Vinel, 2020. "Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network," Papers 2010.01197, arXiv.org.
    16. Zhao, Jiandong & Yu, Zhixin & Yang, Xin & Gao, Ziyou & Liu, Wenhui, 2022. "Short term traffic flow prediction of expressway service area based on STL-OMS," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    17. Palanisamy Manigandan & MD Shabbir Alam & Majed Alharthi & Uzma Khan & Kuppusamy Alagirisamy & Duraisamy Pachiyappan & Abdul Rehman, 2021. "Forecasting Natural Gas Production and Consumption in United States-Evidence from SARIMA and SARIMAX Models," Energies, MDPI, vol. 14(19), pages 1-17, September.
    18. Alexander Frick & George Makrides & Markus Schubert & Matthias Schlecht & George E. Georghiou, 2020. "Degradation Rate Location Dependency of Photovoltaic Systems," Energies, MDPI, vol. 13(24), pages 1-20, December.
    19. Phinikarides, Alexander & Makrides, George & Zinsser, Bastian & Schubert, Markus & Georghiou, George E., 2015. "Analysis of photovoltaic system performance time series: Seasonality and performance loss," Renewable Energy, Elsevier, vol. 77(C), pages 51-63.
    20. Shivshanker Patel & Parthasarathy Ramachandran, 2015. "A Comparison of Machine Learning Techniques for Modeling River Flow Time Series: The Case of Upper Cauvery River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(2), pages 589-602, January.

  7. George E. P. Box, 1957. "Evolutionary Operation: A Method for Increasing Industrial Productivity," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 6(2), pages 81-101, June.

    Cited by:

    1. Fan, Shu-Kai S. & Zahara, Erwie, 2007. "A hybrid simplex search and particle swarm optimization for unconstrained optimization," European Journal of Operational Research, Elsevier, vol. 181(2), pages 527-548, September.
    2. Mishra, SK, 2007. "Performance of Differential Evolution Method in Least Squares Fitting of Some Typical Nonlinear Curves," MPRA Paper 4634, University Library of Munich, Germany.
    3. Yingjie Tang & Zheren Zhang & Zheng Xu, 2020. "Analysis and Design of Damping Circuit Parameters for LCC Valves Based on Broadband Model," Energies, MDPI, vol. 13(5), pages 1-21, February.
    4. Francesco Battaglia, 2001. "Genetic alghorithms, pseudo-random number generators, and Markov chain Monte Carlo methods," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1-2), pages 129-154.
    5. Eltinge John L. & Biemer Paul P. & Holmberg Anders, 2013. "A Potential Framework for Integration of Architecture and Methodology to Improve Statistical Production Systems," Journal of Official Statistics, Sciendo, vol. 29(1), pages 125-145, March.

Chapters

  1. George E. P. Box & Steven C. Hillmer & George C. Tiao, 1978. "Analysis and Modeling of Seasonal Time Series," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 309-344, National Bureau of Economic Research, Inc.

    Cited by:

    1. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Working Papers 0112, Banco de España.
    2. Flaig Gebhard, 2015. "Why We Should Use High Values for the Smoothing Parameter of the Hodrick-Prescott Filter," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 235(6), pages 518-538, December.
    3. Agustín Maravall & Cristophe Planas, 1996. "Estimation Error and the Specification of Unobserved Component Models," Working Papers 9608, Banco de España.
    4. Maravall, Agustín, 2000. "Notes on time serie analysis, ARIMA models and signal extraction," DES - Working Papers. Statistics and Econometrics. WS 10058, Universidad Carlos III de Madrid. Departamento de Estadística.
    5. Marczak, Martyna & Gómez, Víctor, 2012. "Cyclicality of real wages in the USA and Germany: New insights from wavelet analysis," FZID Discussion Papers 50-2012, University of Hohenheim, Center for Research on Innovation and Services (FZID).
    6. Thury, Gerhard & Witt, Stephen F., 1998. "Forecasting industrial production using structural time series models," Omega, Elsevier, vol. 26(6), pages 751-767, December.
    7. Maravall, Agustín, 1999. "Short-term and long-term trends, seasonal and the business cycle," DES - Working Papers. Statistics and Econometrics. WS 6291, Universidad Carlos III de Madrid. Departamento de Estadística.
    8. Maravall, Agustín, 1999. "Seasonal outliers in time series," DES - Working Papers. Statistics and Econometrics. WS 6333, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. John Kuiper, 1978. "A Survey and Comparative Analysis of Various Methods of Seasonal Adjustment," NBER Chapters, in: Seasonal Analysis of Economic Time Series, pages 59-94, National Bureau of Economic Research, Inc.

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