01980nam 2200433 n 450 99639294570331620200824121641.0(CKB)4940000000110718(EEBO)2248499760(UnM)99867373e(UnM)99867373(EXLCZ)99494000000011071819940511d1657 uy |engurbn||||a|bb|The compleat bone-setter[electronic resource] wherein the method of curing broken bones, and strains, and dislocated joynts, together with ruptures, vulgarly called broken bellyes, is fully demonstrated. Whereunto is added The perfect oculist, and The mirrour of health, treating of the pestilence, and all other diseases incident to men, women and children. Also, the acute judgement of urines. /Written originally by Friar Moulton, of the Order of St. Augustine. Now revised, Englished and enlarged by Robert Turner philomathēsLondon: Printed by J.C. for Martha Harison, at the Lamb at the east-end of Pauls.1656. [i.e. 1657][16], 175, [1] p"Philomathēs" in Greek characters on title page."The perfect oculist", "The mirrour of health", and "Of urines" have caption title; register and pagination are continuous.Annotation on Thomason copy: "Jan: 31".Reproductions of the original in the British Library.eebo-0018MedicineEarly works to 1800Materia medicaGreat BritainEarly works to 1800HealthEarly works to 1800MedicineMateria medicaHealthMoulton Thomas1010129Turner Robertfl. 1654-1665.1001757Cu-RivESCu-RivESCStRLINWaOLNBOOK996392945703316The compleat bone-setter2348613UNISA05711oam 2200505Mu 450 991079382480332120200110112915.00-429-51437-90-429-51094-20-429-20232-6(CKB)4100000009930695(MiAaPQ)EBC5986246(OCoLC)1129160328(OCoLC-P)1129160328(FlBoTFG)9780429202322(EXLCZ)99410000000993069520191130d2019 uy 0engurcnu---unuuutxtrdacontentcrdamediacrrdacarrierComputer Intensive Methods in Statistics[electronic resource]Boca Raton CRC Press LLC20191 online resource (227 pages)Description based upon print version of record.0-367-19423-6 0-367-19425-2 Cover; Half Title; Title Page; Copyright Page; Contents; Preface; Introduction; 1. Random Variable Generation; 1.1 Basic Methods; 1.1.1 Congruential Generators; 1.1.2 The KISS Generator; 1.1.3 Beyond Uniform Distributions; 1.2 Transformation Methods; 1.3 Accept-Reject Methods; 1.3.1 Envelope Accept-Reject Methods; 1.4 Problems; 2. Monte Carlo Methods; 2.1 Independent Monte Carlo Methods; 2.1.1 Importance Sampling; 2.1.2 The Rule of Thumb for Importance Sampling; 2.2 Markov Chain Monte Carlo; 2.2.1 Metropolis-Hastings Algorithm; 2.2.2 Special MCMC Algorithms; 2.2.3 Adaptive MCMC2.2.4 Perfect Simulation2.2.5 The Gibbs Sampler; 2.3 Approximate Bayesian Computation Methods; 2.4 Problems; 3. Bootstrap; 3.1 General Principle; 3.1.1 Unified Bootstrap Framework; 3.1.2 Bootstrap and Monte Carlo; 3.1.3 Conditional and Unconditional Distribution; 3.2 Basic Bootstrap; 3.2.1 Plug-in Principle; 3.2.2 Why is Bootstrap Good?; 3.2.3 Example where Bootstrap Fails; 3.3 Bootstrap Confidence Sets; 3.3.1 The Pivotal Method; 3.3.2 Bootstrap Pivotal Methods; 3.3.2.1 Percentile Bootstrap Confidence Interval; 3.3.2.2 Basic Bootstrap Confidence Interval3.3.2.3 Studentized Bootstrap Confidence Interval3.3.3 Transformed Bootstrap Confidence Intervals; 3.3.4 Prepivoting Confidence Set; 3.3.5 BCa-Confidence Interval; 3.4 Bootstrap Hypothesis Tests; 3.4.1 Parametric Bootstrap Hypothesis Test; 3.4.2 Nonparametric Bootstrap Hypothesis Test; 3.4.3 Advanced Bootstrap Hypothesis Tests; 3.5 Bootstrap in Regression; 3.5.1 Model-Based Bootstrap; 3.5.2 Parametric Bootstrap Regression; 3.5.3 Casewise Bootstrap in Correlation Model; 3.6 Bootstrap for Time Series; 3.7 Problems; 4. Simulation-Based Methods; 4.1 EM Algorithm; 4.2 SIMEX; 4.3 Variable Selection4.3.1 F-Backward and F-Forward Procedures4.3.2 FSR-Forward Procedure; 4.3.3 SimSel; 4.4 Problems; 5. Density Estimation; 5.1 Background; 5.2 Histogram; 5.3 Kernel Density Estimator; 5.3.1 Statistical Properties; 5.3.2 Bandwidth Selection in Practice; 5.4 Nearest Neighbor Estimator; 5.5 Orthogonal Series Estimator; 5.6 Minimax Convergence Rate; 5.7 Problems; 6. Nonparametric Regression; 6.1 Background; 6.2 Kernel Regression Smoothing; 6.3 Local Regression; 6.4 Classes of Restricted Estimators; 6.4.1 Ridge Regression; 6.4.2 Lasso; 6.5 Spline Estimators; 6.5.1 Base Splines6.5.2 Smoothing Splines6.6 Wavelet Estimators; 6.6.1 Wavelet Base; 6.6.2 Wavelet Smoothing; 6.7 Choosing the Smoothing Parameter; 6.8 Bootstrap in Regression; 6.9 Problems; References; IndexThis textbook gives an overview of statistical methods that have been developed during the last years due to increasing computer use, including random number generators, Monte Carlo methods, Markov Chain Monte Carlo (MCMC) methods, Bootstrap, EM algorithms, SIMEX, variable selection, density estimators, kernel estimators, orthogonal and local polynomial estimators, wavelet estimators, splines, and model assessment. Computer Intensive Methods in Statistics is written for students at graduate level, but can also be used by practitioners. Features Presents the main ideas of computer-intensive statistical methods Gives the algorithms for all the methods Uses various plots and illustrations for explaining the main ideas Features the theoretical backgrounds of the main methods. Includes R codes for the methods and examples Silvelyn Zwanzig is an Associate Professor for Mathematical Statistics at Uppsala University. She studied Mathematics at the Humboldt- University in Berlin. Before coming to Sweden, she was Assistant Professor at the University of Hamburg in Germany. She received her Ph.D. in Mathematics at the Academy of Sciences of the GDR. Since 1991, she has taught Statistics for undergraduate and graduate students. Her research interests have moved from theoretical statistics to computer intensive statistics. Behrang Mahjani is a postdoctoral fellow with a Ph.D. in Scientific Computing with a focus on Computational Statistics, from Uppsala University, Sweden. He joined the Seaver Autism Center for Research and Treatment at the Icahn School of Medicine at Mount Sinai, New York, in September 2017 and was formerly a postdoctoral fellow at the Karolinska Institutet, Stockholm, Sweden. His research is focused on solving large-scale problems through statistical and computational methods.StatisticsData processingStatisticsData processing.519.502855369Zwanzig Silvelyn517826Mahjani Behrang1518146OCoLC-POCoLC-PBOOK9910793824803321Computer Intensive Methods in Statistics3755531UNINA01868nas 22006015- 450 991089282240332120230321210415.0(CKB)954927668257(CONSER)ce-75073047-(DE-599)ZDB2897879-1(EXLCZ)9995492766825720760115b19751991 -a- aengtxtrdacontentcrdamediacrrdacarrierCanada diseases weekly reportRapport hebdomadaire des maladies au CanadaOttawaBureau of Epidemiology, Laboratory Centre for Disease Control1 online resourcePrint version: Canada diseases weekly report. 0382-232X (DLC)ce 75073047 (OCoLC)2248870 Rapport hebdomadaire des maladies au CanadaCAN DIS WKLY REPCan. dis. wkly. rep.DiseasesCanadaPeriodicalsCommunicable diseasesEpidemiologyCommunicable Disease ControlCommunicable DiseasesEpidemiologyMaladiesCanadaPériodiquesMaladies infectieusesÉpidémiologieDiseasesfast(OCoLC)fst00895158CanadaCanadafastPeriodicalPeriodicals.fastDiseasesCommunicable diseases.Epidemiology.Communicable Disease ControlCommunicable DiseasesEpidemiologyMaladiesMaladies infectieuses.Épidémiologie.Diseases.614.4/271JOURNAL9910892822403321exl_impl conversionCanada diseases weekly report4260161UNINA