Presents basic statistical, regression, and time series definitions and methods
in a practical and easy-to-understand style
Shows how SAS Enterprise Guide can be used to perform time series
analyses, combining step-by-step examples with real-world data to do so
Serves as a valuable reference tool, clearly presenting time series techniques
This is the first book to present time series analysis using the SAS Enterprise Guide software. It
includes some starting background and theory to various time series analysis techniques, and
demonstrates the data analysis process and the final results via step-by-step extensive
illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series
analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of
sectors.
Due 2020-03-29
1st ed. 2020, VIII, 168 p.
95 illus.
Softcover
ISBN 978-981-15-0320-7
Product category :Brief
Series :SpringerBriefs in Statistics
Illustrates a variety of applications of spectral theory to differential
operators, including the Dirichlet Laplacian and Schrödinger operators
Incorporates a brief introduction to functional analysis, with a focus on
unbounded operators and separable Hilbert spaces
This textbook offers a concise introduction to spectral theory, designed for newcomers to
functional analysis. Curating the content carefully, the author builds to a proof of the spectral
theorem in the early part of the book. Subsequent chapters illustrate a variety of application
areas, exploring key examples in detail. Readers looking to delve further into specialized topics
will find ample references to classic and recent literature. Beginning with a brief introduction to
functional analysis, the text focuses on unbounded operators and separable Hilbert spaces as
the essential tools needed for the subsequent theory. A thorough discussion of the concepts of
spectrum and resolvent follows, leading to a complete proof of the spectral theorem for
unbounded self-adjoint operators. Applications of spectral theory to differential operators
comprise the remaining four chapters. These chapters introduce the Dirichlet Laplacian
operator, Schrödinger operators, operators on graphs, and the spectral theory of Riemannian
manifolds. Spectral Theory offers a uniquely accessible introduction to ideas that invite further
study in any number of different directions. A background in real and complex analysis is
assumed; the author presents the requisite tools from functional analysis within the text. This
introductory treatment would suit a functional analysis course intended as a pathway to linear
PDE theory. Independent later chapters allow for flexibility in selecting applications to suit
specific interests within a one-semester course.
Due 2020-04-04
1st ed. 2020, X, 338 p. 31
illus., 30 illus. in color.
Hardcover
ISBN 978-3-030-38001-4
Product category : Graduate/advanced undergraduate textbook
Series : Graduate Texts in Mathematics
Mathematically rigorous but written in a convivial style
Treats the general theory as well as special models of proven interest in
applications
Self-contained with exercises and a helpful appendix on analysis
The ultimate objective of this book is to present a panoramic view of the main stochastic
processes which have an impact on applications, with complete proofs and exercises. Random
processes play a central role in the applied sciences, including operations research, insurance,
finance, biology, physics, computer and communications networks, and signal processing. In
order to help the reader to reach a level of technical autonomy sufficient to understand the
presented models, this book includes a reasonable dose of probability theory. On the other
hand, the study of stochastic processes gives an opportunity to apply the main theoretical
results of probability theory beyond classroom examples and in a non-trivial manner that
makes this discipline look more attractive to the applications-oriented student. One can
distinguish three parts of this book. The first four chapters are about probability theory,
Chapters 5 to 8 concern random sequences, or discrete-time stochastic processes, and the rest
of the book focuses on stochastic processes and point processes. There is sufficient modularity
for the instructor or the self-teaching reader to design a course or a study program adapted to
her/his specific needs. This book is in a large measure self-contained.
Due 2020-04-06
Approx. 670 p.
Softcover
ISBN 978-3-030-40182-5
Product category : Graduate/advanced undergraduate textbook
Series : Universitext
Gives a succinct introduction to necessary mathematical background,
focusing on the results useful for statistics from an otherwise vast
mathematical literature
Presents an up-to-date overview of the state of the art, including some
original results,and discusses open problems
Suitable for self-study or to be used as a graduate level course text
This open access book presents the key aspects of statistics in Wasserstein spaces, i.e. statistics
in the space of probability measures when endowed with the geometry of optimal
transportation. Further to reviewing state-of-the-art aspects, italso provides anaccessible
introduction to the fundamentals of this current topic, as well as an overview thatwill serve as
an invitation and catalyst for further research. Statistics in Wasserstein spaces represents an
emerging topic inmathematical statistics, situated at the interface between functional
dataanalysis (where the data are functions, thus lying in infinite dimensionalHilbert space) and
non-Euclidean statistics (where the data satisfy nonlinearconstraints, thus lying on nonEuclidean manifolds).
The Wassersteinspace provides the natural mathematical formalism to
describe datacollections that are best modeled as random measures on Euclidean space (e.g.
imagesand point processes). Such random measures carry the infinite dimensionaltraits of
functional data, but are intrinsically nonlinear due to positivity andintegrability restrictions.
Indeed, their dominating statistical variationarises through random deformations of an
underlying template, a theme that is pursued in depth in this monograph.
Due 2020-04-19
1st ed. 2020, VIII, 142 p.
27 illus., 23 illus. in color.
Softcover
ISBN 978-3-030-38437-1
Product category : Brief
Series : SpringerBriefs in Probability and Mathematical Statistic
Curves and Surfaces, Conditional Extremes, Curvilinear Integrals,
Complex Functions, Singularities and Fourier Series
Offers an extensive list of completely solved problems in mathematical
analysis
Covers curves and surfaces, conditional extremes, curvilinear integrals,
complex functions, singularities and Fourier series
Additional exercises at the end of each chapter provide rich material for
independent study
This textbook offers an extensive list of completely solved problems in mathematical analysis.
This third of three volumes covers curves and surfaces, conditional extremes, curvilinear
integrals, complex functions, singularities and Fourier series. The series contains the material
corresponding to the first three or four semesters of a course in Mathematical Analysis. Based
on the author years of teaching experience, this work stands out by providing
detailed
solutions (often several pages long) to the problems. The basic premise of the book is that no
topic should be left unexplained, and no question that could realistically arise while studying
the solutions should remain unanswered. The style and format are straightforward and
accessible. In addition, each chapter includes exercises for students to work on independently.
Answers are provided to all problems, allowing students to check their work. Though chiefly
intended for early undergraduate students of Mathematics, Physics and Engineering, the book
will also appeal to students from other areas with an interest in Mathematical Analysis, either
as supplementary reading or for independent study.
Due 2020-04-15
1st ed. 2020, VI, 392 p. 76 illus.
Hardcover
ISBN 978-3-030-38595-8
Product category : Graduate/advanced undergraduate textbook
Series : Problem Books in Mathematics
Explores Euclidean and non-Euclidean geometries, culminating in a
mathematical model for special relativity
Introduces students familiar with calculus to the rigorous foundations of
plane geometry: Euclidean, spherical, hyperbolic, and relativistic
Offers a pathway from classical to abstract geometries by focusing on
isometries
This textbook offers a geometric perspective on special relativity, bridging Euclidean space,
hyperbolic space, and Einstein spacetime in one accessible, self-contained
volume. Using tools
tailored to undergraduates, the author explores Euclidean and non-Euclidean geometries,
gradually building from intuitive to abstract spaces. By the end, readers will have encountered
a range of topics, from isometries to the Lorentzinkowski plane, building
an understanding
of how geometry can be used to model special relativity. Beginning with intuitive spaces, such
as the Euclidean plane and the sphere, a structure theorem for isometries is introduced that
serves as a foundation for increasingly sophisticated topics, such as the hyperbolic plane and
the Lorentzinkowski plane. By gradually introducing tools throughout, the
author offers
readers an accessible pathway to visualizing increasingly abstract geometric concepts.
Numerous exercises are also included with selected solutions provided. Geometry: from
Isometries to Special Relativity offers a unique approach to non-Euclidean geometries,
culminating in a mathematical model for special relativity. The focus on isometries offers
undergraduates an accessible progression from the intuitive to abstract; instructors will
appreciate the complete instructor solutions manual available online. A background in
elementary calculus is assumed.
Due 2020-06-01
1st ed. 2020, IX, 239 p. 72 illus.
Hardcover
ISBN 978-3-030-42100-7
Product category : Undergraduate textbook
Series : Undergraduate Texts in Mathematics