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Catch rope : the long arm of the cowboy
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ISBN: 0585252300 9780585252308 0929398661 9780929398662 1574411136 9781574411133 Year: 1994 Publisher: Denton, TX : University of North Texas Press,

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Orlando di Lasso's imitation magificats for Counter-Reformation Munich
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ISBN: 0691601178 1400863783 0691036144 1306984750 0691630933 9781400863785 9780691036144 9780691601175 Year: 1994 Publisher: Princeton, N. J. Princeton University Press

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After the Mass Ordinary, the Magnificat was the liturgical text most frequently set by Renaissance composers, and Orlando di Lasso's 101 polyphonic settings form the largest and most varied repertory of Magnificats in the history of European music. In the first detailed investigation of this repertory, David Crook focuses on the forty parody or imitation Magnificats, which Lasso based on motets, madrigals, and chansons written by such composers as Josquin and Rore. By examining these Magnificats in their social, historical, and liturgical contexts and in terms of composition theory, Crook opens a new window on the breadth and subtlety of an important composer often harshly judged on his use of preexistent music.Crook places Lasso amidst the Counter-Reformation reforms at the Bavarian court where he composed the Magnificats, and where there emerged a fanatical Marian cult that favored this genre. In a section on compositional procedure, Crook explains that Lasso abandoned the traditional eight psalm-tone melodies in his imitation Magnificats, considers the new ways he found to represent the tones, and describes how Lasso's experimentation reflected the complex relationship between mode and tone in Renaissance theory and practice. Arguing that Lasso's varied uses of preexistent music defy current definitions of parody technique, Crook, in his final chapter, reveals the imitation Magnificats as vastly more imaginative and innovative than previous characterizations suggest.Originally published in 1994.The Princeton Legacy Library uses the latest print-on-demand technology to again make available previously out-of-print books from the distinguished backlist of Princeton University Press. These editions preserve the original texts of these important books while presenting them in durable paperback and hardcover editions. The goal of the Princeton Legacy Library is to vastly increase access to the rich scholarly heritage found in the thousands of books published by Princeton University Press since its founding in 1905.

Garcilaso de la Vega y su escuela poetica
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ISBN: 8430641335 9788430641338 Year: 1983 Volume: vol 133 Publisher: Madrid Taurus


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Garcilaso de la Vega and the material culture of renaissance Europe
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ISBN: 9781442647558 1442647558 1442668490 9781442668492 1442668504 Year: 2014 Publisher: Toronto (Ont.) University of Toronto

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"Garcilaso de la Vega and the Material Culture of Renaissance Europe examines the role of cultural objects in the lyric poetry of Garcilaso de la Vega, the premier poet of sixteenth-century Spain. As a pioneer of the 'new poetry' of Renaissance Europe, aligned with the court, empire, and modernity, Garcilaso was fully attuned to the collection and circulation of luxury artefacts and other worldly goods. In his poems, a variety of objects, including tapestries, paintings, statues, urns, mirrors, and relics participate in lyric acts of discovery and self-revelation, reveal memory as contingent and unstable, expose knowledge of the self as deceptive, and show how history intersects with the ideology of empire."--Publisher's web site

Die Parodiemessen von Orlando di Lasso
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ISBN: 3770520785 Year: 1985 Volume: Band 4 Publisher: München Wilhelm Fink Verlag

Imitacion y transformacion : el petrarquismo en la poesia de Boscan y Garcilaso de la Vega
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ISBN: 1283358832 9786613358837 9027278652 9789027278654 9781556190056 1556190050 9781556190568 1556190565 9789027217394 9027217394 902721736X 9789027217363 1556190050 1556190565 9027217394 9789027217363 Year: 1988 Publisher: Amsterdam Philadelphia Benjamins


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Statistical Inference via Convex Optimization
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ISBN: 9780691200316 0691200319 Year: 2020 Publisher: Princeton, NJ : Princeton University Press,

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"This authoritative book draws on the latest research to explore the interplay of high-dimensional statistics with optimization. Through an accessible analysis of fundamental problems of hypothesis testing and signal recovery, Anatoli Juditsky and Arkadi Nemirovski show how convex optimization theory can be used to devise and analyze near-optimal statistical inferences. Statistical Inference via Convex Optimization is an essential resource for optimization specialists who are new to statistics and its applications, and for data scientists who want to improve their optimization methods. Juditsky and Nemirovski provide the first systematic treatment of the statistical techniques that have arisen from advances in the theory of optimization. They focus on four well-known statistical problems-sparse recovery, hypothesis testing, and recovery from indirect observations of both signals and functions of signals-demonstrating how they can be solved more efficiently as convex optimization problems. The emphasis throughout is on achieving the best possible statistical performance. The construction of inference routines and the quantification of their statistical performance are given by efficient computation rather than by analytical derivation typical of more conventional statistical approaches. In addition to being computation-friendly, the methods described in this book enable practitioners to handle numerous situations too difficult for closed analytical form analysis, such as composite hypothesis testing and signal recovery in inverse problems. Statistical Inference via Convex Optimization features exercises with solutions along with extensive appendixes, making it ideal for use as a graduate text"--


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Lasso Regressions and Forecasting Models in Applied Stress Testing
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ISBN: 1475599323 9781475599329 1475599021 9781475599022 1475599307 Year: 2017 Publisher: [Washington, D.C.]

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Model selection and forecasting in stress tests can be facilitated using machine learning techniques. These techniques have proved robust in other fields for dealing with the curse of dimensionality, a situation often encountered in applied stress testing. Lasso regressions, in particular, are well suited for building forecasting models when the number of potential covariates is large, and the number of observations is small or roughly equal to the number of covariates. This paper presents a conceptual overview of lasso regressions, explains how they fit in applied stress tests, describes its advantages over other model selection methods, and illustrates their application by constructing forecasting models of sectoral probabilities of default in an advanced emerging market economy.

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