LEADER 03656nam 2200661 450 001 9910463729003321 005 20200520144314.0 010 $a0-87421-986-8 035 $a(CKB)2670000000594136 035 $a(EBL)1943263 035 $a(SSID)ssj0001421718 035 $a(PQKBManifestationID)12520521 035 $a(PQKBTitleCode)TC0001421718 035 $a(PQKBWorkID)11423788 035 $a(PQKB)10936884 035 $a(MiAaPQ)EBC3442940 035 $a(OCoLC)903442558 035 $a(MdBmJHUP)muse42293 035 $a(MiAaPQ)EBC1943263 035 $a(Au-PeEL)EBL3442940 035 $a(CaPaEBR)ebr11018349 035 $a(CaONFJC)MIL726383 035 $a(Au-PeEL)EBL1943263 035 $a(EXLCZ)992670000000594136 100 $a20150216h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aVery like a whale $ethe assessment of writing programs /$fEdward M. White, Norbert Elliot, Irvin Peckham 210 1$aBoulder, Colorado :$cUtah State University Press,$d2015. 210 4$dİ2015 215 $a1 online resource (211 p.) 300 $aDescription based upon print version of record. 311 $a0-87421-985-X 311 $a1-322-95101-2 320 $aIncludes bibliographical references and index. 327 $aContents; Introduction; 1. Trends; 2. Lessons; 3. Foundations; 4. Measurement; 5. Design; Glossary; References; About the Authors; Index 330 $a"Written for those who design, redesign, and assess writing programs, Very Like a Whale is an intensive discussion of writing program assessment issues. Taking its title from Hamlet, the book explores the multifaceted forces that shape writing programs and the central role these programs can and should play in defining college education. Given the new era of assessment in higher education, writing programs must provide valid evidence that they are serving students, instructors, administrators, alumni, accreditors, and policymakers. This book introduces new conceptualizations associated with assessment, making them clear and available to those in the profession of rhetoric and composition/writing studies. It also offers strategies that aid in gathering information about the relative success of a writing program in achieving its identified goals. Philosophically and historically aligned with quantitative approaches, White, Elliot, and Peckham use case study and best-practice scholarship to demonstrate the applicability of their innovative approach, termed Design for Assessment (DFA). Well grounded in assessment theory, Very Like a Whale will be of practical use to new and seasoned writing program administrators alike, as well as to any educator involved with the accreditation process"--$cProvided by publisher. 606 $aEnglish language$xRhetoric$xStudy and teaching (Higher)$xEvaluation 606 $aAcademic writing$xStudy and teaching (Higher)$xEvaluation 606 $aReport writing$xStudy and teaching (Higher)$xEvaluation 608 $aElectronic books. 615 0$aEnglish language$xRhetoric$xStudy and teaching (Higher)$xEvaluation. 615 0$aAcademic writing$xStudy and teaching (Higher)$xEvaluation. 615 0$aReport writing$xStudy and teaching (Higher)$xEvaluation. 676 $a808/.0420711 700 $aWhite$b Edward M$g(Edward Michael),$f1933-$01029883 702 $aElliot$b Norbert 702 $aPeckham$b Irvin 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910463729003321 996 $aVery like a whale$92446530 997 $aUNINA LEADER 05427nam 2200649Ia 450 001 9910830956303321 005 20170814180908.0 010 $a1-282-30775-4 010 $a9786612307751 010 $a0-470-31702-7 010 $a0-470-31786-8 035 $a(CKB)1000000000687572 035 $a(EBL)469989 035 $a(OCoLC)476291655 035 $a(SSID)ssj0000343236 035 $a(PQKBManifestationID)11264961 035 $a(PQKBTitleCode)TC0000343236 035 $a(PQKBWorkID)10288447 035 $a(PQKB)11428037 035 $a(MiAaPQ)EBC469989 035 $a(PPN)159316480 035 $a(EXLCZ)991000000000687572 100 $a19981030d1999 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aStatistical modeling by wavelets$b[electronic resource] /$fBrani Vidakovic 210 $aNew York $cWiley$d1999 215 $a1 online resource (410 p.) 225 1 $aWiley series in probability and mathematical statistics. Applied probability and statistics section 300 $a"A Wiley-Interscience publication." 311 $a0-471-29365-2 320 $aIncludes bibliographical references (p. 345-370) and indexes. 327 $aStatistical Modeling by Wavelets; Contents; Preface; Acknowledgments; 1. Introduction; 1.1. Wavelet Evolution; 1.2. Wavelet Revolution; 1.3. Wavelets and Statistics; 1.4. An Appetizer: California Earthquakes; 2. Prerequisites; 2.1. General; 2.2. Hilben Spaces; 2.2.1. Projection Theorem; 2.2.2. 0rthonomal Sets; 2.2.3. Reproducing Kernel Hilberf Spaces; 2.3. Fourier Transformation; 2.3.1. Basic Properties; 2.3.2. Poisson Summation Formula and Sampling Theorem; 2.3.3. Fourier Series; 2.3.4. Discrete Fourier Transform; 2.4. Heisenberg's Uncertainty Principle; 2.5. Some Important Function Spaces 327 $a2.6. Fundanzentals of Signal Processing2.7. Exercises; 3. Wavelets; 3.1. Continuous Wavelet Transformation; 3.1.1. Basic Properties; 3.1.2. Wavelets for Continuous Transfonnations; 3.2. Discretization of the Continuous Wavelet Transform; 3.3. Multiresolution Analysis; 3.3.1. Derivation of a Wavelet Function; 3.4. Same Important Wavelet Bases; 3.4.1. Haar's Wavelets; 3.4.2. Shannon's Wavelets; 3.4.3. Meyer's Wavelets; 3.4.4. Franklin s Wavelets; 3.4.5. Daubechies ' Conzpactly Supporled Wavelets; 3.5. Some Extensions; 3.5.1. Regularity of Wavelets 327 $a3.5.2. The Least Asytnmetric Daubechies ' Wavelets: Symrnlets3.5.3. Approxintations and Characterizations of Functional Spaces; 3.5.4. Daubechies-Lagarias Algorithm; 3.5.5. Moment Conditions; 3.5.6. Interpolating (Cardinal) Wavelets; 3.5.7. Pollen-Type Parameterization of Wavelets; 3.6. Exercises; 4. Discrete Wavelet Transformations; 4.1. Introduction; 4.2. The Cascade Algorithnt; 4.3. The Operator Notation of DWT; 4.3.1. Discrete Wavelet Transfomiations as Linear Transfonnations; 4.4. Exercises; 5. Some Generalizations; 5.1. Coiflets; 5.1.1. Construction of Coifrets 327 $a5.2. Biorthogonal Wavelets5.2.1. Construction of Biorthogonal Wavelets; 5.2.2. B-Spline Wavelets; 5.3. Wavelet Packets; 5.3.1. Basic Properties of Wavelet Packets; 5.3.2. Wavelet Packet Tables; 5.4. Best Basis Selection; 5.4.1. Some Cost Measures and the Best Basis Algorithm; 5.5. ?-Decimated and Stationary Wavelet Transformations; 5.5.1. ?-Decimated Wavelet Transformation; 5.5.2. Stationary (Non-Decimated) Wavelet Transformation; 5.6. Periodic Wavelet Transformations; 5.7. Multivariate Wavelet Transfornations; 5.8. Discussion; 5.9. Exercises; 6. Wavelet Shrinkage; 6.1. Shrinkage Method 327 $a6.2. Lineur Wavelet Regression Estimators6.2.1. Wavelet Kernels; 6.2.2. Local Constant Fit Estimators; 6.3. The Simplest Non-Linear Wavelet Shrinkage: Tliresholding; 6.3.1. Variable Selection and Thresholding; 6.3.2. Oracular Risk for Thresholding Rules; 6.3.3. Why the Wavelet Shrinkage Works; 6.3.4. Almost Sure Convergence of Wavelet Sh rinkuge Est imaf ors; 6.4. General Minimax Paradigm; 6.4.1. Translation of Minimaxity Results to the Wavelet Domain; 6.5. Thresholding Policies and Thresholdkg Rides; 6.5.1. Exact Risk Analysis of Thresholding Rules; 6.5.2. Large Sample Properties 327 $a6.5.3. Some Orher Shrinkage Rules 330 $aA comprehensive, step-by-step introduction to wavelets in statistics.What are wavelets? What makes them increasingly indispensable in statistical nonparametrics? Why are they suitable for ""time-scale"" applications? How are they used to solve such problems as denoising, regression, or density estimation? Where can one find up-to-date information on these newly ""discovered"" mathematical objects? These are some of the questions Brani Vidakovic answers in Statistical Modeling by Wavelets. Providing a much-needed introduction to the latest tools afforded statisticians by wavelet theory, 410 0$aWiley series in probability and mathematical statistics.$pApplied probability and statistics. 606 $aMathematical statistics 606 $aWavelets (Mathematics) 615 0$aMathematical statistics. 615 0$aWavelets (Mathematics) 676 $a515.2433 676 $a519.5 700 $aVidakovic$b Brani$f1955-$0288619 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910830956303321 996 $aStatistical modeling by wavelets$9866473 997 $aUNINA