Examples In Parametric Inference With R
by Ulhas Jayram Dixit /
2016 / English / PDF
4 MB Download
This book discusses examples in parametric inference with R.
Combining basic theory with modern approaches, it presents the
latest developments and trends in statistical inference for
students who do not have an advanced mathematical and statistical
background. The topics discussed in the book are fundamental and
common to many fields of statistical inference and thus serve as
a point of departure for in-depth study. The book is divided into
eight chapters: Chapter 1 provides an overview of topics on
sufficiency and completeness, while Chapter 2 briefly discusses
unbiased estimation. Chapter 3 focuses on the study of moments
and maximum likelihood estimators, and Chapter 4 presents bounds
for the variance. In Chapter 5, topics on consistent estimator
are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies
some more powerful tests. Lastly, Chapter 8 examines unbiased and
other tests.
This book discusses examples in parametric inference with R.
Combining basic theory with modern approaches, it presents the
latest developments and trends in statistical inference for
students who do not have an advanced mathematical and statistical
background. The topics discussed in the book are fundamental and
common to many fields of statistical inference and thus serve as
a point of departure for in-depth study. The book is divided into
eight chapters: Chapter 1 provides an overview of topics on
sufficiency and completeness, while Chapter 2 briefly discusses
unbiased estimation. Chapter 3 focuses on the study of moments
and maximum likelihood estimators, and Chapter 4 presents bounds
for the variance. In Chapter 5, topics on consistent estimator
are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies
some more powerful tests. Lastly, Chapter 8 examines unbiased and
other tests.
Senior undergraduate and graduate students in statistics and
mathematics, and those who have taken an introductory course in
probability, will greatly benefit from this book. Students are
expected to know matrix algebra, calculus, probability and
distribution theory before beginning this course. Presenting a
wealth of relevant solved and unsolved problems, the book offers
an excellent tool for teachers and instructors who can assign
homework problems from the exercises, and students will find the
solved examples hugely beneficial in solving the exercise
problems.
Senior undergraduate and graduate students in statistics and
mathematics, and those who have taken an introductory course in
probability, will greatly benefit from this book. Students are
expected to know matrix algebra, calculus, probability and
distribution theory before beginning this course. Presenting a
wealth of relevant solved and unsolved problems, the book offers
an excellent tool for teachers and instructors who can assign
homework problems from the exercises, and students will find the
solved examples hugely beneficial in solving the exercise
problems.