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 LEADER 04049nam 2200673Ia 450 001 9910172243503321 005 20200520144314.0 010 $a1-282-08676-6 010 $a9786612086762 010 $a1-4008-2806-6 024 7 $a10.1515/9781400828067 035 $a(CKB)1000000000756327 035 $a(EBL)445568 035 $a(OCoLC)336817288 035 $a(SSID)ssj0000114153 035 $a(PQKBManifestationID)11131776 035 $a(PQKBTitleCode)TC0000114153 035 $a(PQKBWorkID)10102074 035 $a(PQKB)10802662 035 $a(MdBmJHUP)muse36503 035 $a(DE-B1597)447019 035 $a(OCoLC)979725977 035 $a(DE-B1597)9781400828067 035 $a(Au-PeEL)EBL445568 035 $a(CaPaEBR)ebr10284246 035 $a(CaONFJC)MIL208676 035 $a(MiAaPQ)EBC445568 035 $a(EXLCZ)991000000000756327 100 $a20070830d2006 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aBounded rationality and policy diffusion $esocial sector reform in Latin America /$fKurt Weyland 205 $aCourse Book 210 $aPrinceton, NJ $cPrinceton University Press$dc2006 215 $a1 online resource (312 p.) 300 $aDescription based upon print version of record. 311 $a0-691-12974-6 311 $a0-691-13471-5 320 $aIncludes bibliographical references (p. [239]-281) and index. 327 $tFront matter --$tContents --$tPreface --$tAbbreviations --$tChapter 1. The Puzzle of Policy Diffusion --$tChapter 2. Toward a New Theory of Policy Diffusion --$tChapter 3. External Pressures and International Norms in Pension Reform --$tChapter 4. Cognitive Heuristics in the Diffusion of Pension Reform --$tChapter 5. External Pressures and International Norms in Health Reform --$tChapter 6. Cognitive Heuristics in the Diffusion of Health Reform --$tChapter 7. Bounded Rationality in the Era of Globalization --$tReferences and Interviews --$tIndex 330 $aWhy do very different countries often emulate the same policy model? Two years after Ronald Reagan's income-tax simplification of 1986, Brazil adopted a similar reform even though it threatened to exacerbate income disparity and jeopardize state revenues. And Chile's pension privatization of the early 1980's has spread throughout Latin America and beyond even though many poor countries that have privatized their social security systems, including Bolivia and El Salvador, lack some of the preconditions necessary to do so successfully. In a major step beyond conventional rational-choice accounts of policy decision-making, this book demonstrates that bounded--not full--rationality drives the spread of innovations across countries. When seeking solutions to domestic problems, decision-makers often consider foreign models, sometimes promoted by development institutions like the World Bank. But, as Kurt Weyland argues, policymakers apply inferential shortcuts at the risk of distortions and biases. Through an in-depth analysis of pension and health reform in Bolivia, Brazil, Costa Rica, El Salvador, and Peru, Weyland demonstrates that decision-makers are captivated by neat, bold, cognitively available models. And rather than thoroughly assessing the costs and benefits of external models, they draw excessively firm conclusions from limited data and over extrapolate from spurts of success or failure. Indications of initial success can thus trigger an upsurge of policy diffusion. 606 $aDecision making$zLatin America$vCase studies 606 $aPolicy sciences 607 $aLatin America$xSocial policy$vCase studies 615 0$aDecision making 615 0$aPolicy sciences. 676 $aRE/361.61098 686 $a88.62$2bcl 700 $aWeyland$b Kurt Gerhard$0929475 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910172243503321 996 $aBounded rationality and policy diffusion$92089365 997 $aUNINA