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Mean squared error
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61	Regularized Regression David M. Blei Columbia University December 3, 2014 Modern regression problems are high dimensional, which means that the number of covariates p is large. In practice statisticians regularize their Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Econometrics Least squares Parametric statistics Regularization Linear regression Tikhonov regularization Elastic net regularization Mean squared error Maximum likelihood Statistics Estimation theory Regression analysis
62	Methods in Ecology and Evolution 2015, 6, 412–423 doi: 210XSPECIAL FEATURE NEW OPPORTUNITIES AT THE INTERFACE BETWEEN ECOLOGY AND STATISTICS Add to Reading List Source URL: prism.oregonstate.edu Language: English - Date: 2015-04-13 19:30:57 Regression dilution Mean squared error Generalized linear model Logistic regression Multivariate adaptive regression splines Instrumental variable Variance inflation factor Statistics Regression analysis Linear regression
63	A Small Sample Bias Correction and Implications for Inference Brenton R Clarke1∗ and Christopher J Milne2 1 Mathematics and Statistics, School of Engineering Add to Reading List Source URL: www.statistics.gov.hk Language: English - Date: 2013-08-22 04:39:21 Statistical inference M-estimators Data analysis Estimator Normal distribution Maximum likelihood Robust statistics Consistent estimator Mean squared error Statistics Estimation theory Statistical theory
64	Conditional Design Effects for SEM estimates DL Oberski Tilburg University, The Netherlands Abstract Add to Reading List Source URL: www.statistics.gov.hk Language: English - Date: 2013-08-22 04:39:21 Estimation theory Parametric statistics Statistical methods Errors-in-variables models Structural equation modeling Mean squared error Linear regression Standard error Normal distribution Statistics Regression analysis Econometrics
65	A Comprehensive Comparison of Uncertainty Analysis Tools Add to Reading List Source URL: www.isgmax.com Language: English - Date: 2009-12-18 11:59:00 Uncertainty Variance Errors and residuals in statistics Normal distribution Estimation theory Standard deviation Statistical hypothesis testing Observational error Mean squared error Statistics Measurement Measurement uncertainty
66	Auxiliary Material Auxiliary Material for the Paper: Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): Chemistry Evaluation and Initial Results Add to Reading List Source URL: www.geosci-model-dev-discuss.net Language: English - Date: 2014-12-04 09:02:20 Regression analysis Data analysis Root-mean-square deviation Mean squared error Errors and residuals in statistics Forecasting Nainital Statistics Measurement Estimation theory
67	Analyzing Uncertainty for Risk Management1 Howard Castrup, Ph.D. President, Integrated Sciences Group Bakersfield, CASummary Add to Reading List Source URL: www.isgmax.com Language: English - Date: 2009-11-19 22:38:00 Uncertainty Errors and residuals in statistics Normal distribution Observational error Confidence interval Standard deviation Mean squared error Variance Statistical hypothesis testing Statistics Measurement Measurement uncertainty
68	Adaptive Monte Carlo via Bandit Allocation James Neufeld JNEUFELD @ UALBERTA . CA Andr´as Gy¨orgy GYORGY @ UALBERTA . CA Dale Schuurmans Add to Reading List Source URL: webdocs.cs.ualberta.ca Language: English - Date: 2014-05-18 17:42:18 Monte Carlo methods Statistical theory Decision theory Estimator M-estimator Importance sampling Mean squared error Bias of an estimator Regret Statistics Estimation theory Statistical inference
69	Statistical Policy Working Paper 4 - Glossary of Nonsampling Error Terms: An Illustration of a Semantic Problem in Statistics Add to Reading List Source URL: fcsm.sites.usa.gov Language: English - Date: 2014-05-28 18:13:51 Sampling Standard error Error Mean squared error Observational error Systematic error Estimator Intelligence quotient Statistics Measurement Sampling error
70	Estimating the Total Fertility Rate from Multiple Imperfect Data Sources and Assessing its Uncertainty Leontine Alkema, Adrian E. Raftery, Patrick Gerland, Samuel J. Clark, Fran¸cois Pelletier 1 Add to Reading List Source URL: www.csss.washington.edu Language: English - Date: 2009-01-07 17:16:32 Econometrics Regression analysis Data analysis Bootstrapping Linear regression Cross-validation Variance Mean squared error Estimator Statistics Statistical inference Estimation theory

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