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The second edition of Robust Statistical Methods with R provides a
systematic treatment of robust procedures with an emphasis on new
developments and on the computational aspects. There are many
numerical examples and notes on the R environment, and the updated
chapter on the multivariate model contains additional material on
visualization of multivariate data in R. A new chapter on robust
procedures in measurement error models concentrates mainly on the
rank procedures, less sensitive to errors than other procedures.
This book will be an invaluable resource for researchers and
postgraduate students in statistics and mathematics. Features *
Provides a systematic, practical treatment of robust statistical
methods * Offers a rigorous treatment of the whole range of robust
methods, including the sequential versions of estimators, their
moment convergence, and compares their asymptotic and finite-sample
behavior * The extended account of multivariate models includes the
admissibility, shrinkage effects and unbiasedness of two-sample
tests * Illustrates the small sensitivity of the rank procedures in
the measurement error model * Emphasizes the computational aspects,
supplies many examples and illustrations, and provides the own
procedures of the authors in the R software on the book's website
The second edition of Robust Statistical Methods with R provides a
systematic treatment of robust procedures with an emphasis on new
developments and on the computational aspects. There are many
numerical examples and notes on the R environment, and the updated
chapter on the multivariate model contains additional material on
visualization of multivariate data in R. A new chapter on robust
procedures in measurement error models concentrates mainly on the
rank procedures, less sensitive to errors than other procedures.
This book will be an invaluable resource for researchers and
postgraduate students in statistics and mathematics. Features *
Provides a systematic, practical treatment of robust statistical
methods * Offers a rigorous treatment of the whole range of robust
methods, including the sequential versions of estimators, their
moment convergence, and compares their asymptotic and finite-sample
behavior * The extended account of multivariate models includes the
admissibility, shrinkage effects and unbiasedness of two-sample
tests * Illustrates the small sensitivity of the rank procedures in
the measurement error model * Emphasizes the computational aspects,
supplies many examples and illustrations, and provides the own
procedures of the authors in the R software on the book's website
This book collects peer-reviewed contributions on modern
statistical methods and topics, stemming from the third workshop on
Analytical Methods in Statistics, AMISTAT 2019, held in Liberec,
Czech Republic, on September 16-19, 2019. Real-life problems demand
statistical solutions, which in turn require new and profound
mathematical methods. As such, the book is not only a collection of
solved problems but also a source of new methods and their
practical extensions. The authoritative contributions focus on
analytical methods in statistics, asymptotics, estimation and
Fisher information, robustness, stochastic models and inequalities,
and other related fields; further, they address e.g. average
autoregression quantiles, neural networks, weighted empirical
minimum distance estimators, implied volatility surface estimation,
the Grenander estimator, non-Gaussian component analysis, meta
learning, and high-dimensional errors-in-variables models.
This book collects peer-reviewed contributions on modern
statistical methods and topics, stemming from the third workshop on
Analytical Methods in Statistics, AMISTAT 2019, held in Liberec,
Czech Republic, on September 16-19, 2019. Real-life problems demand
statistical solutions, which in turn require new and profound
mathematical methods. As such, the book is not only a collection of
solved problems but also a source of new methods and their
practical extensions. The authoritative contributions focus on
analytical methods in statistics, asymptotics, estimation and
Fisher information, robustness, stochastic models and inequalities,
and other related fields; further, they address e.g. average
autoregression quantiles, neural networks, weighted empirical
minimum distance estimators, implied volatility surface estimation,
the Grenander estimator, non-Gaussian component analysis, meta
learning, and high-dimensional errors-in-variables models.
Five years after the onset of the global financial crisis, Europe's
economy is still fragile. Notwithstanding recent positive signs
amid calmer financial markets, medium-term growth is likely to
remain frail owing to continuing weaknesses and vulnerabilities at
the country level and in the fabric of European institutions and
banks, especially in the euro area. In addition, unemployment in
many countries has reached very high levels. The IMF research
collected in this volume provides a number of guideposts that offer
an opportunity for stronger and better-balanced growth and
employment in Europe after what has been a long and dismal period
of crisis.
Financial globalization has increased dramatically over the past
three decades, particularly for advanced economies, while emerging
market and developing countries experienced more moderate
increases. Divergences across countries stem from different capital
control regimes, and factors such as institutional quality and
domestic financial development. Although, in principle, financial
globalization should enhance international risk sharing, reduce
macroeconomic volatility, and foster economic growth, in practice
its effects are less clear-cut. This paper envisages a gradual and
orderly sequencing of external financial liberalization and
complementary reforms in macroeconomic policy framework as
essential components of a successful liberalization strategy.
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