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1. |
Record Nr. |
UNINA9910460309803321 |
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Autore |
Crowley Stephen <1960-> |
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Titolo |
Hot coal, cold steel [[electronic resource] ] : Russian and Ukrainian workers from the end of the Soviet Union to the post-communist transformations / / Stephen Crowley |
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Pubbl/distr/stampa |
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Ann Arbor, : University of Michigan Press, c1997 |
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ISBN |
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1-282-64465-3 |
9786612644658 |
0-472-02692-5 |
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Descrizione fisica |
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1 online resource (288 p.) |
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Disciplina |
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Soggetti |
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Coal miners - Labor unions - Political activity - Russia (Federation) |
Coal Miners' Strike, Soviet Union, 1989 |
Coal miners - Labor unions - Political activity - Ukraine |
Iron and steel workers - Labor unions - Political activity - Russia (Federation) |
Iron and steel workers - Labor unions - Political activity - Ukraine |
Electronic books. |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Bibliographic Level Mode of Issuance: Monograph |
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Nota di bibliografia |
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Includes bibliographical references (p. 207-273) and index. |
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Nota di contenuto |
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Workers, collective action, and political movements -- The 1989 miners strike -- Steelworkers and mutual dependence -- Steelworkers, workers organizations, and collective action -- Building a workers movement -- From economics to politics -- Labor and economic transformation -- Politics and coal miners in Ukraine and Russia -- Workers, politics, and the post-communist transformations. |
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2. |
Record Nr. |
UNINA9910438140003321 |
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Autore |
Golyandina Nina |
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Titolo |
Singular Spectrum Analysis for Time Series / / by Nina Golyandina, Anatoly Zhigljavsky |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
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ISBN |
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1-299-19783-3 |
3-642-34913-7 |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (125 p.) |
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Collana |
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SpringerBriefs in Statistics, , 2191-5458 |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Statistics |
Statistical Theory and Methods |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Introduction: Preliminaries -- SSA Methodology and the Structure of the Book -- SSA Topics Outside the Scope of this Book -- Common Symbols and Acronyms -- Basic SSA: The Main Algorithm -- Potential of Basic SSA -- Models of Time Series and SSA Objectives -- Choice of Parameters in Basic SSA -- Some Variations of Basic SSA -- SSA for Forecasting, interpolation, Filtration and Estimation: SSA Forecasting Algorithms -- LRR and Associated Characteristic Polynomials -- Recurrent Forecasting as Approximate Continuation -- Confidence Bounds for the Forecast -- Summary and Recommendations on Forecasting Parameters -- Case Study: ‘Fortified Wine’ -- Missing Value Imputation -- Subspace-Based Methods and Estimation of Signal Parameters -- SSA and Filters. |
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Sommario/riassunto |
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Singular spectrum analysis (SSA) is a technique of time series analysis and forecasting combining elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA seeks to decompose the original series into a sum of a small number of interpretable components such as trend, oscillatory components and noise. It is based on the singular value decomposition of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity are assumed for the time series. This makes SSA a model-free method and hence enables SSA to |
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have a very wide range of applicability. The present book is devoted to the methodology of SSA and shows how to use SSA both safely and with maximum effect. Potential readers of the book include: professional statisticians and econometricians, specialists in any discipline in which problems of time series analysis and forecasting occur, specialists in signal processing and those needed to extract signals from noisy data, and students taking courses on applied time series analysis. |
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