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[null,"1.","Record Nr.",null,"UNINA9910970757503321"]
[null,null,"Autore",null,"Honda Jirō"]
[null,null,"Titolo",null,"Do IMF Programs Improve Economic Governance? / / Jiro Honda"]
[null,null,"Pubbl/distr/stampa",null,"Washington, D.C. : , : International Monetary Fund, , 2008"]
[null,null,"ISBN",null,"9786612840685"]
[{"$nulls":4},"9781462318469"]
[{"$nulls":4},"1462318460"]
[{"$nulls":4},"9781452733951"]
[{"$nulls":4},"1452733953"]
[{"$nulls":4},"9781451869743"]
[{"$nulls":4},"1451869746"]
[{"$nulls":4},"9781282840683"]
[{"$nulls":4},"1282840681"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (34 p.)"]
[null,null,"Collana",null,"IMF Working Papers"]
[{"$nulls":4},"IMF working paper ; ; WP/08/114"]
[null,null,"Disciplina",null,"353.00722"]
[null,null,"Soggetti",null,"Economic assistance - Developing countries - Econometric models"]
[{"$nulls":4},"Economic development - Developing countries - Econometric models"]
[{"$nulls":4},"Administrative Processes in Public Organizations"]
[{"$nulls":4},"Banking"]
[{"$nulls":4},"Banks and Banking"]
[{"$nulls":4},"Banks and banking"]
[{"$nulls":4},"Banks"]
[{"$nulls":4},"Bureaucracy"]
[{"$nulls":4},"Corporate crime"]
[{"$nulls":4},"Corruption"]
[{"$nulls":4},"Criminology"]
[{"$nulls":4},"Depository Institutions"]
[{"$nulls":4},"Discrete Regression and Qualitative Choice Models"]
[{"$nulls":4},"Discrete Regressors"]
[{"$nulls":4},"Econometric models"]
[{"$nulls":4},"Econometrics & economic statistics"]
[{"$nulls":4},"Econometrics"]
[{"$nulls":4},"Income"]
[{"$nulls":4},"Legal support in revenue administration"]
[{"$nulls":4},"Macroeconomics"]
[{"$nulls":4},"Micro Finance Institutions"]
[{"$nulls":4},"Mortgages"]
[{"$nulls":4},"Personal income"]
[{"$nulls":4},"Personal Income, Wealth, and Their Distributions"]
[{"$nulls":4},"Probit models"]
[{"$nulls":4},"Proportions"]
[{"$nulls":4},"Public finance & taxation"]
[{"$nulls":4},"Public Finance"]
[{"$nulls":4},"Revenue"]
[{"$nulls":4},"Taxation, Subsidies, and Revenue: General"]
[{"$nulls":4},"White-collar crime"]
[{"$nulls":4},"Montenegro"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,null,"Nota di contenuto",null,"Table of Contents; I. Background; II. The IMF's Approach to Economic Governance; A. The Approach; B. Literature Review; III. Stylized Facts About Economic Governance in Developing Countries; IV. Empirical Framework; A. Model; B. Data; C. Results; V. Concluding Remarks"]
[null,null,null,"Sommario/riassunto","This paper examines the effects of IMF financial assistance on economic governance in developing countries, based on panel data analyses of perceived governance indicators. It uses a two-stage approach to address possible endogeneity issues. The results show that successful implementation of IMF programs is associated with improvements in the quality of economic governance. Specifically, the paper finds statistically robust results that IMF concessional programs through the Poverty Reduction and Growth Facility tend to enhance the rule of law and strengthen control of corruption. Through this exercise, however, no statistically significant effect is observed for assistances under the General Resource Account."]
[null,"2.","Record Nr.",null,"UNINA9911031669703321"]
[null,null,"Autore",null,"Franc Alain"]
[null,null,"Titolo",null,"Linear Dimensionality Reduction / / by Alain Franc"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-95785-7"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (281 pages)"]
[null,null,"Collana",null,"Lecture Notes in Statistics, , 2197-7186 ; ; 228"]
[null,null,"Disciplina",null,"519.535"]
[null,null,"Soggetti",null,"Multivariate analysis"]
[{"$nulls":4},"Big data"]
[{"$nulls":4},"Machine learning"]
[{"$nulls":4},"Multivariate Analysis"]
[{"$nulls":4},"Big Data"]
[{"$nulls":4},"Statistical Learning"]
[{"$nulls":4},"Anàlisi multivariable"]
[{"$nulls":4},"Dades massives"]
[{"$nulls":4},"Aprenentatge automàtic"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"- 1. Introduction -- 2. Principal Component Analysis (PCA) -- 3. Complements on PCA -- 4. PCA with Metrics on Rows and Columns -- 5. Correspondence Analysis -- 6. PCA with Instrumental Variables -- 7. Canonical Correlation Analysis -- 8. Multiple Canonical Correlation Analysis -- 9. Multidimensional Scaling."]
[null,null,null,"Sommario/riassunto","This book provides an overview of some classical linear methods in Multivariate Data Analysis. This is an old domain, well established since the 1960s, and refreshed timely as a key step in statistical learning. It can be presented as part of statistical learning, or as dimensionality reduction with a geometric flavor. Both approaches are tightly linked: it is easier to learn patterns from data in low-dimensional spaces than in high-dimensional ones. It is shown how a diversity of methods and tools boil down to a single core method, PCA with SVD, so that the efforts to optimize codes for analyzing massive data sets like distributed memory and task-based programming, or to improve the efficiency of algorithms like Randomized SVD, can focus on this shared core method, and benefit all methods. This book is aimed at graduate "]
[{"$nulls":4},"students and researchers working on massive data who have encountered the usefulness of linear dimensionality reduction and are looking for a recipe to implement it. It has been written according to the view that the best guarantee of a proper understanding and use of a method is to study in detail the calculations involved in implementing it. With an emphasis on the numerical processing of massive data, it covers the main methods of dimensionality reduction, from linear algebra foundations to implementing the calculations. The basic requisite elements of linear and multilinear algebra, statistics and random algorithms are presented in the appendix."]