LEADER 05228nam 22006374a 450 001 9910829983303321 005 20230617003003.0 010 $a1-280-27069-1 010 $a9786610270699 010 $a0-470-30004-3 010 $a0-470-85700-5 010 $a0-470-85699-8 035 $a(CKB)1000000000356086 035 $a(EBL)228320 035 $a(OCoLC)475936031 035 $a(SSID)ssj0000104765 035 $a(PQKBManifestationID)11133615 035 $a(PQKBTitleCode)TC0000104765 035 $a(PQKBWorkID)10086541 035 $a(PQKB)10984702 035 $a(MiAaPQ)EBC228320 035 $a(EXLCZ)991000000000356086 100 $a20040825d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aApplied optimal designs$b[electronic resource] /$fedited by Martijn P.F. Berger, Weng Kee Wong 210 $aHoboken, NJ $cWiley$dc2005 215 $a1 online resource (313 p.) 300 $aDescription based upon print version of record. 311 $a0-470-85697-1 320 $aIncludes bibliographical references and index. 327 $aApplied Optimal Designs; Contents; List of Contributors; Editors' Foreword; 1 Optimal Design in Educational Testing; 1.1 Introduction; 1.1.1 Paper-and-pencil or computerized adaptive testing; 1.1.2 Dichotomous response; 1.1.3 Polytomous response; 1.1.4 Information functions; 1.1.5 Design problems; 1.2 Test Design; 1.2.1 Fixed-form test design; 1.2.2 Test design for CAT; 1.3 Sampling Design; 1.3.1 Paper-and-pencil calibration; 1.3.2 CAT calibration; 1.4 Future Directions; Acknowledgements; References; 2 Optimal On-line Calibration of Testlets; 2.1 Introduction; 2.2 Background 327 $a2.2.1 Item response functions2.2.2 D-optimal design criterion; 2.3 Solution for Optimal Designs; 2.3.1 Mathematical programming model; 2.3.2 Unconstrained conjugate-gradient method; 2.3.3 Constrained conjugate-gradient method; 2.3.4 Gradient of log det M(B; Q, x); 2.3.5 MCMC sequential estimation of item parameters; 2.3.6 Note on performance measures; 2.4 Simulation Results; 2.5 Discussion; Appendix A Derivation of the Gradient of log det M(B; Q, x); Appendix B Projection on the Null Space of the Constraint Matrix; Acknowledgements; References 327 $a3 On the Empirical Relevance of Optimal Designs for the Measurement of Preferences3.1 Introduction; 3.2 Conjoint Analysis; 3.3 Paired Comparison Models in Conjoint Analysis; 3.4 Design Issues; 3.5 Experiments; 3.5.1 Experiment 1; 3.5.2 Experiment 2; 3.6 Discussion; Acknowledgements; References; 4 Designing Optimal Two-stage Epidemiological Studies; 4.1 Introduction; 4.2 Illustrative Examples; 4.2.1 Example 1; 4.2.2 Example 2; 4.2.3 Example 3; 4.3 Meanscore; 4.3.1 Example of meanscore; 4.4 Optimal Design and Meanscore; 4.4.1 Optimal design derivation for fixed second stage sample size 327 $a4.4.2 Optimal design derivation for fixed budget4.4.3 Optimal design derivation for fixed precision; 4.4.4 Computational issues; 4.5 Deriving Optimal Designs in Practice; 4.5.1 Data needed to compute optimal designs; 4.5.2 Examples of optimal design; 4.5.3 The optimal sampling package; 4.5.4 Sensitivity of design to sampling variation in pilot data; 4.6 Summary; 4.7 Appendix 1 Brief Description of Software Used; 4.7.1 R language; 4.7.2 S-PLUS; 4.7.3 STATA; 4.8 Appendix 2 The Optimal Sampling Package; 4.8.1 Illustrative data sets; 4.9 Appendix 3 Using the Optimal Package in R 327 $a4.9.1 Syntax and features of optimal sampling command 'budget' in R4.9.2 Example; 4.10 Appendix 4 Using the Optimal Package in S-Plus; 4.11 Appendix 5 Using the Optimal Package in STATA; 4.11.1 Syntax and features of 'optbud' function in STATA; 4.11.2 Analysis with categorical variables; 4.11.3 Illustrative example; References; 5 Response-Driven Designs in Drug Development; 5.1 Introduction; 5.2 Motivating Example: Quantal Models for Dose Response; 5.2.1 Optimality criteria; 5.3 Continuous Models; 5.3.1 Example 3.1; 5.3.2 Example 3.2 327 $a5.4 Variance Depending on Unknown Parameters and Multi-response Models 330 $aThere is an increasing need to rein in the cost of scientific study without sacrificing accuracy in statistical inference. Optimal design is the judicious allocation of resources to achieve the objectives of studies using minimal cost via careful statistical planning. Researchers and practitioners in various fields of applied science are now beginning to recognize the advantages and potential of optimal experimental design. Applied Optimal Designs is the first book to catalogue the application of optimal design to real problems, documenting its widespread use across disciplines as diver 606 $aOptimal designs (Statistics) 606 $aExperimental design 615 0$aOptimal designs (Statistics) 615 0$aExperimental design. 676 $a519.5/7 676 $a519.57 701 $aBerger$b Martijn$01659694 701 $aWong$b Weng Kee$01659695 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829983303321 996 $aApplied optimal designs$94014465 997 $aUNINA