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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.
This volume collects authoritative contributions on analytical
methods and mathematical statistics. The methods presented include
resampling techniques; the minimization of divergence; estimation
theory and regression, eventually under shape or other constraints
or long memory; and iterative approximations when the optimal
solution is difficult to achieve. It also investigates probability
distributions with respect to their stability, heavy-tailness,
Fisher information and other aspects, both asymptotically and
non-asymptotically. The book not only presents the latest
mathematical and statistical methods and their extensions, but also
offers solutions to real-world problems including option pricing.
The selected, peer-reviewed contributions were originally presented
at the workshop on Analytical Methods in Statistics, AMISTAT 2015,
held in Prague, Czech Republic, November 10-13, 2015.
This volume collects authoritative contributions on analytical
methods and mathematical statistics. The methods presented include
resampling techniques; the minimization of divergence; estimation
theory and regression, eventually under shape or other constraints
or long memory; and iterative approximations when the optimal
solution is difficult to achieve. It also investigates probability
distributions with respect to their stability, heavy-tailness,
Fisher information and other aspects, both asymptotically and
non-asymptotically. The book not only presents the latest
mathematical and statistical methods and their extensions, but also
offers solutions to real-world problems including option pricing.
The selected, peer-reviewed contributions were originally presented
at the workshop on Analytical Methods in Statistics, AMISTAT 2015,
held in Prague, Czech Republic, November 10-13, 2015.
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