Maximum Penalized Likelihood Estimation by Eggermont Paul (30 results)

Condition: New. pp. 532.

Condition: New. pp. 532 52:B&W 6.14 x 9.21in or 234 x 156mm (Royal 8vo) Case Laminate on White w/Gloss Lam.

Condition: New. pp. 532.

Condition: New.

Paperback. Condition: new. Paperback. This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in s- tistics, operationsresearch, andappliedmathematics, aswellasresearchers and practitioners in the ?eld. The present volume was supposed to have a short chapter on nonparametric regression but was intended to deal mainly with inverse problems. However, the chapter on nonparametric regression kept growing to the point where it is now the only topic covered. Perhaps there will be a Volume III. It might even deal with inverse problems. But for now we are happy to have ?nished Volume II. The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. We study smoothing splines and local polynomials in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known. The new twist is thatlettingtheinnerproductdependonthesmoothingparameteropensup new possibilities: It leads to asymptotically equivalent reproducing kernel estimators (without quali?cations) and thence, via uniform error bounds for kernel estimators, to uniform error bounds for smoothing splines and, via strong approximations, to con?dence bands for the unknown regression function. ItcameassomewhatofasurprisethatreproducingkernelHilbert space ideas also proved useful in the study of local polynomial estimators. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condition: New. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Series: Springer Series in Statistics. Num Pages: 592 pages, biography. BIC Classification: PBC; TJF; UYQV. Category: (G) General (US: Trade). Dimension: 235 x 155 x 30. Weight in Grams: 896. . 2011. Paperback. . . . .

Condition: New. SUPER FAST SHIPPING.

Condition: New.

Condition: New.

Condition: New. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Series: Springer Series in Statistics. Num Pages: 592 pages, biography. BIC Classification: PBC; TJF; UYQV. Category: (G) General (US: Trade). Dimension: 235 x 155 x 30. Weight in Grams: 896. . 2011. Paperback. . . . . Books ship from the US and Ireland.

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Hardcover. Condition: new. Hardcover. This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. We cover both theory and practice of nonparametric estimation. The text is novel in its use of maximum penalized likelihood estimation, and the theory of convex minimization problems (fully developed in the text) to obtain convergence rates. We also use (and develop from an elementary view point) discrete parameter submartingales and exponential inequalities. A substantial effort has been made to discuss computational details, and to include simulation studies and analyses of some classical data sets using fully automatic (data driven) procedures. Some theoretical topics that appear in textbook form for the first time are definitive treatments of I.J. Good's roughness penalization, monotone and unimodal density estimation, asymptotic optimality of generalized cross validation for spline smoothing and analogous methods for ill-posed least squares problems, and convergence proofs of EM algorithms for random sampling problems. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

hardcover. Condition: New. In shrink wrap. Looks like an interesting title!

Condition: New. In.

Condition: As New. Unread book in perfect condition.

Condition: New. Series: Springer Series in Statistics. Num Pages: 530 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 27. Weight in Grams: 807. . 2010. Softcover reprint of hardcover 1st ed. 2001. Paperback. . . . .

Condition: New. Deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints such as unimodality and log-concavity. This book focuses on convexity and convex optimization, as applied to maximum penalized likelihood estimation. Series: Springer Series in Statistics. Num Pages: 530 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 28. Weight in Grams: 917. . 2001. Hardback. . . . .

Paperback. Condition: new. Paperback. This is the second volume of a text on the theory and practice of maximum penalized likelihood estimation. It is intended for graduate students in s- tistics, operationsresearch, andappliedmathematics, aswellasresearchers and practitioners in the ?eld. The present volume was supposed to have a short chapter on nonparametric regression but was intended to deal mainly with inverse problems. However, the chapter on nonparametric regression kept growing to the point where it is now the only topic covered. Perhaps there will be a Volume III. It might even deal with inverse problems. But for now we are happy to have ?nished Volume II. The emphasis in this volume is on smoothing splines of arbitrary order, but other estimators (kernels, local and global polynomials) pass review as well. We study smoothing splines and local polynomials in the context of reproducing kernel Hilbert spaces. The connection between smoothing splines and reproducing kernels is of course well-known. The new twist is thatlettingtheinnerproductdependonthesmoothingparameteropensup new possibilities: It leads to asymptotically equivalent reproducing kernel estimators (without quali?cations) and thence, via uniform error bounds for kernel estimators, to uniform error bounds for smoothing splines and, via strong approximations, to con?dence bands for the unknown regression function. ItcameassomewhatofasurprisethatreproducingkernelHilbert space ideas also proved useful in the study of local polynomial estimators. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condition: New. Series: Springer Series in Statistics. Num Pages: 530 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 27. Weight in Grams: 807. . 2010. Softcover reprint of hardcover 1st ed. 2001. Paperback. . . . . Books ship from the US and Ireland.

Condition: New. Deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints such as unimodality and log-concavity. This book focuses on convexity and convex optimization, as applied to maximum penalized likelihood estimation. Series: Springer Series in Statistics. Num Pages: 530 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 28. Weight in Grams: 917. . 2001. Hardback. . . . . Books ship from the US and Ireland.

Hardcover. Condition: new. Hardcover. This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. We cover both theory and practice of nonparametric estimation. The text is novel in its use of maximum penalized likelihood estimation, and the theory of convex minimization problems (fully developed in the text) to obtain convergence rates. We also use (and develop from an elementary view point) discrete parameter submartingales and exponential inequalities. A substantial effort has been made to discuss computational details, and to include simulation studies and analyses of some classical data sets using fully automatic (data driven) procedures. Some theoretical topics that appear in textbook form for the first time are definitive treatments of I.J. Good's roughness penalization, monotone and unimodal density estimation, asymptotic optimality of generalized cross validation for spline smoothing and analogous methods for ill-posed least squares problems, and convergence proofs of EM algorithms for random sampling problems. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Condition: New. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Series: Springer Series in Statistics. Num Pages: 592 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 241 x 158 x 46. Weight in Grams: 982. . 2008. Hardback. . . . . Books ship from the US and Ireland.

Condition: New. Ideal for researchers and practitioners in statistics and industrial mathematics, this book covers the theory and practice of nonparametric estimation. It is novel in its use of maximum penalized likelihood estimation and convex minimization problem theory. Series: Springer Series in Statistics. Num Pages: 592 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational. Dimension: 241 x 158 x 46. Weight in Grams: 982. . 2008. Hardback. . . . .

Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fully develops the theory of convex minimization problems to obtain convergence ratesIncludes simulation studies and analyses of classical data sets using fully automatic (data driven) proceduresMany topics appear for the first time in text.

Paperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 913.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Fully develops the theory of convex minimization problems to obtain convergence ratesIncludes simulation studies and analyses of classical data sets using fully automatic (data driven) proceduresMany topics appear for the first time in text.