Cited by 1,103Statistical Methods and InferenceStatistical Methods and Bayesian InferenceStatistical Methods and Applications

The DOI matches its Crossref record; 116 of the 117 references it cites were found in a registry or an index.

Model Uncertainty, Data Mining and Statistical Inference

Chatfield, Chris·1995·Journal of the Royal Statistical Society. Series A (Statistics in Society)

DOI 10.2307/2983440

Open this paper in CoChat — free account

Abstract

This paper takes a broad, pragmatic view of statistical inference to include all aspects of model formulation. The estimation of model parameters traditionally assumes that a model has a prespecified known form and takes no account of possible uncertainty regarding the model structure. This implicitly assumes the existence of a 'true' model, which many would regard as a fiction. In practice model uncertainty is a fact of life and likely to be more serious than other sources of uncertainty which have received far more attention from statisticians. This is true whether the model is specified on subject-matter grounds or, as is increasingly the case, when a model is formulated, fitted and checked on the same data set in an iterative, interactive way. Modern computing power allows a large number of models to be considered and data-dependent specification searches have become the norm in many areas of statistics. The term data mining may be used in this context when the analyst goes to great lengths to obtain a good fit. This paper reviews the effects of model uncertainty, such as too narrow prediction intervals, and the non-trivial biases in parameter estimates which can follow data-based modelling. Ways of assessing and overcoming the effects of model uncertainty are discussed, including the use of simulation and resampling methods, a Bayesian model averaging approach and collecting additional data wherever possible. Perhaps the main aim of the paper is to ensure that statisticians are aware of the problems and start addressing the issues even if there is no simple, general theoretical fix.

Integrity signals

Registry

Crossref · 10.2307/2983440

Cites

116 of 116 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

An Introduction to the Bootstrap
Efron, B., & Tibshirani, R. J. (1994) · doi:10.1201/9780429246593 · Cited by 40,500
Book Review: Time Series Analysis: Forecasting and Control
Geurts, M. D., Box, G. E. P., & Jenkins, G. M. (1977) · Journal of Marketing Research · doi:10.2307/3150485 · Cited by 19,410
Statistical Methods for Research Workers
Fisher, R. A. (1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,883
The Logic of Scientific Discovery
Bhaskar, R. (2013) · Routledge eBooks · doi:10.4324/9780203090732-9 · Cited by 7,765
The Advanced Theory of Statistics.
Clarke, G. M., Kendall, M. G., & Stuart, A. (1978) · Journal of the Royal Statistical Society Series A (General) · doi:10.2307/2344782 · Cited by 6,727
A leisurely look at the bootstrap, the jackknife, and cross-validation
Efron, B., & Gong, G. (1983) · The American Statistician · doi:10.1080/00031305.1983.10483087 · Cited by 3,229
Science and Statistics
Box, G. E. P. (1976) · Journal of the American Statistical Association · doi:10.1080/01621459.1976.10480949 · Cited by 2,114
Bayesian Forecasting and Dynamic Models
West, M., & Harrison, J. (1997) · Springer eBooks · doi:10.1007/b98971 · Cited by 1,946
Cross-Validation of Regression Models
Picard, R. R., & Cook, R. D. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478083 · Cited by 1,551
Combining forecasts: A review and annotated bibliography
(1990) · Long Range Planning · doi:10.1016/0024-6301(90)90316-v · Cited by 1,508

Flagged first, then by citations

Show all 117 references

1 of the 117 references couldn’t be matched to a known paper.

Sorted by citations

Items 801–900 of 1,000

Chapter 1 Design and evaluation of environmental radioactivity models
Scott, E. M. (2003) · Radioactivity in the environment · doi:10.1016/s1569-4860(03)80057-8 · Cited by 2
Sensitivity analysis for a forest growth model: A statistical and time-dependent point of view
Song, X., & Zhao, G. (2012) · International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA) · doi:10.1109/pma.2012.6524857 · Cited by 2
Accurate phenotyping: Reconciling approaches through Bayesian model averaging
Chen, C. C. M., Keith, J. M., & Mengersen, K. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176136 · Cited by 2
What are the Odds!? The “Airport Fallacy” and Statistical Inference
Gunter, B., & Tong, C. (2017) · Significance · doi:10.1111/j.1740-9713.2017.01057.x · Cited by 2
Measuring the Effects of Data Mining on Inference
Burr, T. L., & Tobin, S. J. (2014) · Advances in information quality and management · doi:10.4018/978-1-4666-5888-2.ch176 · Cited by 2
Comparing Two Classical Methods of Detecting Multicollinearity in Financial and Economic Time Series Data
Akintunde, M. O., Olawale, A. O., Simeon, A. A., & Azeez, A. I. A. (2021) · International Journal of Applied Mathematics and Theoretical Physics · doi:10.11648/j.ijamtp.20210703.11 · Cited by 2
The multidimensional analysis of social exclusion
Bailey, N., Fahmy, E., & Bradshaw, J. (2017) · Policy Press eBooks · doi:10.1332/policypress/9781447334224.003.0014 · Cited by 2
Methodological Progress in On-Farm Regional Agronomic Diagnosis: A Review
Doré, T., Clermont‐Dauphin, C., Crozat, Y., David, C., Jeuffroy, M., Loyce, C., Makowski, D., Malézieux, É., Meynard, J.-M., & Valantin‐Morison, M. (2009) · Springer eBooks · doi:10.1007/978-90-481-2666-8_45 · Cited by 2
Exploring the Potential for Predicting Project Dispute Resolution Satisfaction Using Logistic Regression
Cheung, S. O., & Yiu, T. W. (2014) · Springer eBooks · doi:10.1007/978-3-319-04429-3_5 · Cited by 2
Bayesian Model Averaging in Modeling of State Specific Failure Rates in HIV/AIDS Progression
Mwirigi, N., Simwa, R. O., Wainaina, M., & Sewe, S. (2022) · Mathematics and Statistics · doi:10.13189/ms.2022.100409 · Cited by 2
High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords
Chardon, N. I., Nabe‐Nielsen, J., Assmann, J. J., Jacobsen, I. B. D., Guéguen, M., Normand, S., & Wipf, S. (2022) · Diversity and Distributions · doi:10.1111/ddi.13498 · Cited by 2
Identifying key sectors of sustainable development: A Bayesian framework estimating policy‐impacts in a general equilibrium
Ziesmer, J. (2024) · Agribusiness · doi:10.1002/agr.21889 · Cited by 2
The multidimensional analysis of social exclusion
Bailey, N., Fahmy, E., & Bradshaw, J. (2017) · Policy Press eBooks · doi:10.51952/9781447334262.ch013 · Cited by 2
Risk Score for Predicting Adolescent Mental Health Problems Among Children Using Parental Report Only: The TRAILS Study
Burger, H., Boks, M. P., Hartman, C. A., Aukes, M. F., Verhulst, F. C., Ormel, J., & Reijneveld, S. A. (2014) · Academic Pediatrics · doi:10.1016/j.acap.2014.07.006 · Cited by 2
AUTOREG: A Computer Program Library for Dynamic Econometric Models With Autoregressive Errors
Hendry, D. F., & Srba, F. (2000) · doi:10.1093/0198293542.003.0015 · Cited by 2
Estimating the Reliability of Consumer Electronics from User Survey Data and Test Data
Shafiei, N., Herrmann, J. W., Krive, A., & Modarres, M. (2022) · Annual Reliability and Maintainability Symposium (RAMS) · doi:10.1109/rams51457.2022.9893921 · Cited by 2
Does juvenile herring (Clupea harengus) affect the capelin (Mallotus villosus) recruitment in the Barents Sea? - A model study for the years 2001 - 2003 focusing on capelin larvae mortality, spawning sites and drift patterns
Wiedmann, M. (2010) · NORA - Norwegian Open Research Archives · Cited by 2
Progress in Intelligent Data Analysis
Liu, X. (1999) · Applied Intelligence · doi:10.1023/a:1008384708180 · Cited by 2
Finding Pathways: Case Selection for Studying Causal Mechanisms in Mixed-Methods ResearchPreprint
Barnes, J., & Weller, N. (2012) · SSRN Electronic Journal · doi:10.2139/ssrn.2150372 · Cited by 2
International Comparability and Quality of Statistics
Depoutot, R., & Arondel, P. (1999) · Contributions to statistics · doi:10.1007/978-3-642-48863-4_6 · Cited by 2
Cross-Country Growth Empirics and Model Uncertainty: An OverviewPreprint
Ulasan, B. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1973861 · Cited by 2
Evaluation of methods for interval estimation of model outputs, with application to survival models
Stevens, R. (2003) · Journal of Applied Statistics · doi:10.1080/0266476032000076100 · Cited by 2
INFORMATION BASED MODEL AVERAGING AND INTERNAL METANALYSIS IN SEEMINGLY UNRELATED REGRESSIONS WITH AN APPLICATION TO A DEMAND SYSTEMPreprint
Bryant, H. L., & Davis, G. C. (2003) · RePEc: Research Papers in Economics · doi:10.22004/ag.econ.21918 · Cited by 2
Frequentist Model-Averaged Confidence Intervals
Turek, D. (2013) · Otago University Research Archive (University of Otago) · Cited by 2
Incorporating Model Uncertainty and Model Instability in Forecasting Bond Risk Premia and Term Structure of Government Bond Yield – A Bayesian Model Averaging ApproachPreprint
Zhou, H. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1948869 · Cited by 2
Interpreting Econometric Evidence: The Behaviour of Consumers' Expenditure in the United Kingdom
Hendry, D. F., & Davidson, J. E. H. (2000) · doi:10.1093/0198293542.003.0011 · Cited by 2
Reversals of Least-Squares Estimates and Model-Independent Estimation for Directions of Unique EffectsPreprint
Knaeble, B., & Dutter, S. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.02722 · Cited by 3
Model Combination and Stock Return PredictabilityPreprint
Hagmann, M., & Loebb, J. (2006) · SSRN Electronic Journal · doi:10.2139/ssrn.897810 · Cited by 2
Bayesian inference under model misspecification using transport-Lagrangian distances: an application to seismic inversionPreprint
Scarinci, A., Fehler, M. C., & Marzouk, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.07027 · Cited by 2
A multi-scale analysis of forest structure and vertebrate diversity.
Psyllakis, J. M. (2007) · doi:10.24124/2007/bpgub444 · Cited by 2
Discussion of the Paper by Walley
(1996) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1996.tb02066.x · Cited by 2
Uncertainty in aquatic toxicological exposure-effect models: the toxicity of 2,4-dichlorophenoxyacetic acid and 4-chlorophenol to daphnia carinata
Dixon, W. (2010) · RMIT Research Repository (RMIT University Library) · doi:10.25439/rmt.27340395 · Cited by 2
Współczesne kluczowe problemy modelowania systemów kanalizacyjnych
Zawilski, M., & Sakson, G. (2009) · Prace Naukowe Politechniki Warszawskiej. Inżynieria Środowiska · Cited by 2
On the separation of classes: can the Fisher criterion be improved upon when classes have unequal variance–covariance structure?
Åberg, K. M., & Jacobsson, S. P. (2010) · Journal of Chemometrics · doi:10.1002/cem.1326 · Cited by 1
Correlation
Manfred Mudelsee, A. (2014) · Atmospheric and oceanographic sciences library · doi:10.1007/978-3-319-04450-7_7 · Cited by 1
Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns
Guidolin, M., & Pedio, M. (2022) · Forecasting · doi:10.3390/forecast4010016 · Cited by 1
Partitioning Uncertainty in Model Predictions from Compartmental Modeling of Global Carbon Cycle
Gazioğlu, S. (2024) · Mathematical and Computational Applications · doi:10.3390/mca29040047 · Cited by 1
Ambiguous landscapes: A framework for assessing robustness and uncertainties in archaeological point pattern analysis
Malatesta, E. H., & de Valeriola, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307743 · Cited by 1
The Gut Check: On Stability and Homogeneity Methods for Reference Materials
Gierz, K. (2025) · Journal of Statistical Theory and Practice · doi:10.1007/s42519-025-00490-y · Cited by 1
Long-lag VARs
Graeve, F. D., & Westermark, A. (2025) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2025.103831 · Cited by 1
Deep Learning for Tumor Progression in Glioblastoma: A Comprehensive Evaluation for Clinical Diagnosis
Khan, R. A., Lambert, P. J., Wang, Z., Abbasian, P., Ryner, L. N., Pitz, M. W., & Ashraf, A. (2025) · International Conference on Biomedical Signal and Image Processing (ICBIP) · doi:10.1109/icbip66535.2025.11284424 · Cited by 1
Estimating municipal road lighting parameters with linear and support vector regression models
Bhattacharya, S., Satvaya, P., & Ayaz, R. (2026) · Journal of Engineering Research · doi:10.1016/j.jer.2026.02.023 · Cited by 1
Post-vaccination serological status predicts protection against heterologous SAT1 foot-and-mouth disease virus challenge in goats
Fosgate, G. T., Oerlemans, D. R., Seoke, L., Opperman, P. A., Lazarus, D. D., Gobiye, M., & Heath, L. E. (2026) · Preventive Veterinary Medicine · doi:10.1016/j.prevetmed.2026.106893 · Cited by 1
Bootstrap and Cross-Validation to Assess Complexity of Data-Driven Regression Models
Sauerbrei, W., & Schumacher, M. (2007) · Springer eBooks · doi:10.1007/3-540-39949-6_29 · Cited by 1
Learning short multivariate time series models through evolutionary and sparse matrix computation
Swift, S., Kok, J. N., & Liu, X. (2006) · Natural Computing · doi:10.1007/s11047-006-9005-9 · Cited by 1
Optimal dynamic management of energy systems: implementations and empirical analysis
Giacomo, L. D. (2012) · Energy Systems · doi:10.1007/s12667-012-0066-9 · Cited by 1
Three of the Authors Reply
Weng, H., Messam, L. L. M., & Hertz‐Picciotto, I. (2009) · American Journal of Epidemiology · doi:10.1093/aje/kwp269 · Cited by 1
Predicting Hydromantes shastae occurrences in Shasta County, California
Mooney, T. L. (2015) · doi:10.25549/usctheses-m3558 · Cited by 1
SPECIAL FEATURE: DATA ASSIMILATION The role of data assimilation in predictive ecology
Niu, S., Luo, Y., Dietze, M. C., Keenan, T. F., Shi, Z., Li, J., Chapin, F. S., & Li, J. N. (2014) · Cited by 1
The Role of Physical and Human Capital Formation
Knight, J., & Ding, S. (2012) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199698691.003.0006 · Cited by 1
A DTN Routing Algorithm Based on Traffic Prediction
Wei, Y., & Wang, J. (2015) · International Conference on Intelligent Networks and Intelligent Systems · doi:10.1109/icinis.2015.24 · Cited by 1
Information and Economics: A New Way to Think About Expectations and to Improve Economic Prediction 1
Tuckett, D., Ormerod, P., Nyman, R., & Smith, R. E. (2015) · Cited by 1
Expert Consensus on Predictive Parameters for the Occurrence of a Fall and a Contralateral Hip Fracture 1 and 3 Years After Hip Fracture: A Delphi Survey
Joeris, A., Hurtado‐Chong, A., Blauth, M., Goldhahn, J., Bodmer, N. S., & Bachmann, L. M. (2017) · Geriatric Orthopaedic Surgery & Rehabilitation · doi:10.1177/2151458516687811 · Cited by 1
Prediction of Sales on Some Large-Scale Retailing Types in South Korea
Jeong, D. (2017) · The East Asian Journal of Business Management · doi:10.13106/eajbm.2017.vol7.no4.35 · Cited by 1
Difference or not to difference an integrated time series? An empirical investigation
Chee-Yin, Y., Lim, H. E., & Duan, H. (2018) · Economic Research-Ekonomska Istraživanja · doi:10.1080/1331677x.2018.1484783 · Cited by 1
Modelling momentum winner/loser asymmetry: the sources of winner and loser returns in the ASX200 and S&P500
Inglis, N., Vanstone, B. J., & Hahn, T. (2019) · Accounting and Finance · doi:10.1111/acfi.12452 · Cited by 1
Modelación recursiva gruesa y la decisión de política monetaria en México
Rodríguez, A., & Rodríguez, P. N. (2007) · doi:10.36095/banxico/di.2007.04 · Cited by 1
Doing Without Thinking
Smith, G. (2018) · Oxford University Press eBooks · doi:10.1093/oso/9780198824305.003.0004 · Cited by 1
Frequentist Inference
Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_2 · Cited by 1
Flow regime alteration effects on the organic C dynamics in semiarid stream ecosystems
Acuña, V. (2010) · Springer eBooks · doi:10.1007/978-94-007-0608-8_16 · Cited by 1
Model Selection
Wang, Y. (2011) · Springer eBooks · doi:10.1007/978-3-642-21551-3_16 · Cited by 1
Economics
Ormerod, P. (2017) · Understanding complex systems · doi:10.1007/978-3-319-42424-8_2 · Cited by 1
Econometric Analysis in Hospitality and Tourism Management
Yang, Y., Abrate, G., & Ai, C. (2023) · Emerald Group Publishing Limited eBooks · doi:10.1108/978-1-80455-063-220231005 · Cited by 1
The t-Distribution Approach to the Second-Order Multiresponse Surface Model of Paracetamol Tablets Quality Characteristics
Sylvi, P., Purhadi, P., Fithriasari, K., & Sutikno, S. (2023) · Applied Sciences · doi:10.3390/app13063951 · Cited by 1
Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): Explanation and Elaboration. Translation into Russian
Moons, K. G. M., Altman, D. G., Reitsma, J. B., loannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, P. G. S. (2023) · Вопросы современной педиатрии · doi:10.15690/vsp.v22i2.2557 · Cited by 1
Coastal generalized ecosystem model (CGEM) 1.0: Flexible model formulations for simulating complex biogeochemical processes in aquatic ecosystems
Jarvis, B. M., Lehrter, J. C., Lowe, L. L., Penta, B., Wan, Y., Duvall, M. S., Simmons, C., Melendez, W., & Ko, D. S. (2024) · Ecological Modelling · doi:10.1016/j.ecolmodel.2024.110831 · Cited by 1
Phenomenological Modeling of Diverse and Heterogeneous Synaptic Dynamics at Natural Density
Korcsak-Gorzo, A., Linssen, C., Albers, J., Dasbach, S., Duarte, R., Kunkel, S., Morrison, A., Senk, J., Stapmanns, J., Tetzlaff, T., et al. (2024) · Neuromethods · doi:10.1007/978-1-0716-4019-7_15 · Cited by 1
Uncertainty-aware Spatio-Temporal Human Mobility Modeling and Anomaly Detection
Wen, H., Cao, S., Rasheed, Z., Shafique, K. H., & Akoglu, L. (2025) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/3748636.3762741 · Cited by 1
Nonparametric Regression with Multiple Predictors
Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_12 · Cited by 1
Hypothesis Testing and Variable Selection
Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_4 · Cited by 1
Bayesian Multimodal Evidence Computation by Adapti Tempering MCMC
Wu, M.-D., & Fitzgerald, W. J. (1996) · Springer eBooks · doi:10.1007/978-94-011-5430-7_18 · Cited by 1
Bootstrapping Uncertainty in Image Analysis
Archer, G. E., & Chan, K. (1996) · COMPSTAT · doi:10.1007/978-3-642-46992-3_19 · Cited by 1
Regression and Random Confounding
Knaeble, B. (2015) · Electronic journal of applied statistical analysis · doi:10.1285/i20705948v8n3p346 · Cited by 1
Binary Data Models
Wakefield, J. (2012) · Springer series in statistics · doi:10.1007/978-1-4419-0925-1_7 · Cited by 1
concepts, opportunities and challenges
Lawless, J. F. (2016) · Cited by 1
Assess the heart disease risk of the Chinese elderly using a predictive model
Fu, Y. P. (2020) · Advances in Social Sciences Research Journal · doi:10.14738/assrj.72.7911 · Cited by 1
Robust and Misspecification Resistant Model Selection in Regression Models with Information Complexity and Genetic Algorithms
Liu, Y. (2007) · Cited by 1
Cross‐estimation for decision selection
Gu, X., & Li, B. (2020) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2542 · Cited by 1
DETECÇÃO E MATERIALIZAÇÃO DE CONLUIO EM LICITAÇÕES DE OBRAS PÚBLICAS
Signor, R., Oliveira, A., Lopes, A. D. O., Belarmino, A. T. N., Raupp, A. B., Vallim, J. J. C. B., & Oliveira, P. D. S. (2020) · Revista Brasileira de Ciências Policiais · doi:10.31412/rbcp.v11i3.664 · Cited by 1
Inferring latent heterogeneity using many feature variables supervised by survival outcome
Jia, B., Zeng, D., Liao, J. J. Z., Liu, G. F., Tan, X. M., Diao, G., & Ibrahim, J. G. (2021) · Statistics in Medicine · doi:10.1002/sim.8972 · Cited by 1
Transport Lagrangian misfit measures and velocity model uncertainty in Bayesian moment tensor inversion
Scarinci, A., Marzouk, Y., Gu, C., Fehler, M. C., bin Waheed, U., Kaka, S. I., & Dia, B. M. (2021) · Second International Meeting for Applied Geoscience & Energy · doi:10.1190/segam2021-3594806.1 · Cited by 1
Uncertainty Quantification with Extreme Learning Machine
Guignard, F. (2022) · Springer theses · doi:10.1007/978-3-030-95231-0_6 · Cited by 1
Exogeneity
Hendry, D. F., Engle, R. F., & Richard, J. (2000) · doi:10.1093/0198293542.003.0016 · Cited by 1
Association between short-term exposure to air pollution and COVID-19 mortality in all German districts: the importance of confounders
Miller, G., Menzel, A., & Ankerst, D. P. (2022) · Environmental Sciences Europe · doi:10.1186/s12302-022-00657-5 · Cited by 1
Modelo para el tratamiento de conjuntos complejos con datos ausentes de variables categóricas en un contexto de e-democracia. Aplicación a encuestas de opinión
del Castillo, P. R. (2012) · doi:10.20868/upm.thesis.14754 · Cited by 1
Inflation Fan Charts and Different Dimensions of Uncertainty: What if Macroeconomic Uncertainty is High?Preprint
Kowalczyk, H. (2013) · SSRN Electronic Journal · doi:10.2139/ssrn.2320486 · Cited by 1
Digital Mapping Techniques '09-workshop proceedings, Morgantown, West Virginia, May 10-13, 2009
Soller, D. R. (2011) · Antarctica A Keystone in a Changing World · doi:10.3133/ofr20101335 · Cited by 1
Bayesian Model Averaging in Multi-Factor MarketsPreprint
Franke, M. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1930916 · Cited by 1
Model, Choice of
Matthews, J. N. S. (2014) · Wiley StatsRef: Statistics Reference Online · doi:10.1002/9781118445112.stat05763 · Cited by 1
Array methods in statistics with applications to the modelling and forecasting of mortality
Kirkby, J. G. (2009) · OpenGrey (Institut de l'Information Scientifique et Technique) · Cited by 1
Integrating GIS, Circular Statistics and KDSD for Modelling Spatial Data: A Case Study
Aguilar, A. M. (2002) · Geographical and Environmental Modelling · doi:10.1080/13615930220127260 · Cited by 1
Making our measurements count
Brighton, M. (2000) · Medical Teacher · doi:10.1080/01421590078571 · Cited by 1
Practical Issues in and Applications of Clinical Data Mining
Olaleye, D., & Tardiff, B. E. (2001) · Drug Information Journal · doi:10.1177/009286150103500317 · Cited by 1
Estimating and Correcting the Effects of Model Selection Uncertainty
Tsague, G. L. E. N. (2006) · doi:10.53846/goediss-3679 · Cited by 1
Application of Time Series in Predicting the Water Levels of the Akosombo Dam
Mensah, D. K. (2013) · Cited by 1
Analytics for Improved Cancer Screening and Treatment
Silberholz, J. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 1
National and global structural macroeconometric modelling
Garratt, A., Lee, K. C., Pesaran, M. H., & Shin, Y. (2006) · doi:10.1093/0199296855.003.0003 · Cited by 1
Postscript: The Econometrics of PC‐GIVE
Hendry, D. F. (2000) · doi:10.1093/0198293542.003.0020 · Cited by 1
Unraveling the paradox of external knowledge sourcing in an era of open innovation, appropriation, and organizational learning
Lorenz, A. (2014) · DepositOnce · doi:10.14279/depositonce-4095 · Cited by 1
Reliability and Uncertainty in Diffusion MRI Modelling
Charles, C. N. (2016) · The Sydney eScholarship Repository (The University of Sydney) · Cited by 1

Authors: Chatfield, C.
Affiliations: University of Bath (GB)
Corresponding author: Chatfield, Chris
Venue: Journal of the Royal Statistical Society. Series A (Statistics in Society)
Publisher: Oxford University Press (OUP)
Published: 1995
Volume: 158
Issue: 3
Pages: 419
ISSN: 0964-1998 (ISSN-L)
DOI: 10.2307/2983440
OpenAlex: W2060050130
Cited by: 1,103
References: 117
Citations by year: 2026: 18 · 2025: 34 · 2024: 38 · 2023: 33 · 2022: 35 · 2021: 33 · 2020: 44 · 2019: 28 · 2018: 47 · 2017: 35 · 2016: 37 · 2015: 51 · 2014: 52 · 2013: 28 · 2012: 63
FWCI: 24.46
Type: journal article
Language: en
Open access: closed
Locations: Journal of the Royal Statistical Society Series A (Statistics in Society) (publishedVersion)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
Keywords: model uncertainty, data mining, statistical inference, model selection, prediction intervals, estimation bias, Bayesian model averaging, resampling, simulation, model misspecification

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Model Uncertainty, Data Mining and Statistical Inference

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks