
Statistics books by astronomers and
physicists
 Bayesian
Logical Data Analysis for the Physical Sciences
by P. C. Gregory (2005). After introduction to probabilities,
Bayesian
and frequentist inference, this monograph presents Bayesian
applications to data with Gaussian and Poisson errors, linear and
nonlinear modelling, maximum entropy, Markov chain Monte Carlo
integration, and spectral analysis of time series. Provides worked
examples with astronomical data, problem sets, and codes associated
with Mathematica.
 Multivariate
Data Analysis for Astrophysics
by Fionn Murtagh (c. 2005).
Unpublished lecture notes for a graduate course for astronomers with
astronomical applications emphasizing clustering (hierarchical, minimal
spanning tree, Voronoi tesselation, partitioning,
mixture models, selforganizing maps, ultrametric spaces, Padic
coding),
and discriminant analysis (Bayes and nonparametric discrimination,
multilayer
perceptron). An update of his 1987 monograph appears here.
 Bootstrap
Techniques for Signal Processing
by Abdelhak M. Zoubir and
D. Robert Iskander (2004).
This book serves as a handbook on `bootstrap' for engineers, to analyze
complicated data with little or no model assumptions. Bootstrap has
found many applications in engineering field including, artificial
neural networks, biomedical engineering, environmental engineering,
image processing, and Radar and sonar signal processing.
Majority of the applications are taken from signal processing
literature.
 Practical
Statistics for Astronomers
by Jasper V. Wall & C. R. Jenkins (2003). Textbook on
methods relevant for observational astronomy with emphasis on Bayesian
approaches and worked problems. Covers probability, correlation,
hypothesis testing, Bayesian models, time series analysis, luminosity
functions and clustering.
 Data
Reduction and Error Analysis for the Physical Sciences
by Philip Bevington & D. Keith Robinson (2003, 2nd
ed.). Popular text with code covering error analysis, Monte Carlo
techniques, leastsquares fitting, maximumlikelihood, and
goodnessoffit.
 Astronomical
Image and Data Analysis
by JeanLuc Starck and Fionn Murtagh (2002). Advanced
techniques for treating astronomical data including data filtering
& storage, image processing (edge detection, segmentation, pattern
recognition), image compression, source detectcion, multiscale analysis
using wavelet transforms, deconvolution, multivariate data, entropies,
catalogs and Virtual Observatories.
 Statistics
of the Galaxy Distribution
by Vicent J. Martinez and Enn Saar (2002). Comprehensive
monograph on the largescale galaxy of the universe traced by galaxy
redshift surveys. It reviews the astronomical observations,
statistical techniques and cosmological inferences.
 Principles
of Data Analysis
by Prasenjit Saha. Brief introductory book covering
Gaussian & Poisson distributions, Monte Carlo methods, least
squares, nonlinear regression, and entropy. Available free on the
Web.
 Probability
and Statistics in Experimental Physics
by Byron P. Roe (2001, 2nd edition) Textbook covering
basic
comcepts, statistical distributions, Monte Carlo methods, central limit
theorem, correlation coefficients, curve fitting with constraints
andconfidence belts, likelihood ratios, leastsquares and robust
estimation (including
errors in x and y), Poisson problems. Problem sets with
solutions.
 Data
Analysis: Statistical and Computational Methods for Scientists and
Engineers
by Siegmund Brandt (1999, 3rd edition). Volume
bridging the gap between physical experiment and statistical
theory. Includes Monte Carlo, maximum likelihood and least
squares methods; hypothesis tests, analysis of variance, polynomial
regression, and time series analysis. Code on CDROM.
 Statistical
Data
Analysis
by Glen Cowan (1998). Guide to practical applications of
statistics in expermental physical science with examples from particle
physics. Includes Monte Carlo methods; parameter estimation
(maximum likelihood, least squares, moments); errors, limits and
confidence intervals; and characteristic functions.
 Image
processing and Data Analysis: The Multiscale Approach
by J.L. Starck, F. Murtagh & A. Bijaoui (1998).
Technical monograph on the use of wavelets for multiscale analysis of
astronomical, engineering, remote sensing, and medical images.
 Astrostatistics
by G. Jogesh Babu & Eric D. Feigelson (1996). A review
of research topics on the methodology of astronomical data analysis
including resampling methods, spatial point processes, symmetrical
linear regressions, multivariate classification, time series analysis,
censoring and truncation. Introduces astronomical problems to
statisticians.
 Data
Analysis:
A Bayesian Tutorial
by Devinder Sivia (1996). A clear introduction by a
physicist covering parameter estimation, model selection, assigning
probabilities, nonparametric estimation, and experimental design.
 Statistics:
A Guide to the Use of Statistical Methods in the Physical Sciences
by Roger J. Barlow (1993). Introductory treatment of
parameter estimation (least squares, maximum likelihood), hypothesis
testing,
Bayesian statistics and nonparametric methods.
 Statistics
in Theory and Practice
by Robert Lupton (1993). Intermediatelevel monograph aimed
at physical scientists explaining probability distributions, sampling
statistics, confidence intervale, hypothesis testing, maximum
likelihood estimation, goodnessoffit, and nonparametric rank
tests. Includes problems with answers.
 A
Practical Guide to Data Analysis for Physical Science Students
by Louis Lyons (1991). Undergraduate text for
interpretation of experiments including distributions and moments,
Gaussian errors, combining results, leastsquares fitting, weighting
and confidence intervals, parameter testing.
Some older volumes of interest include:
Statistics for
nuclear and particle physicists (Louis Lyons, 1986)
E. T. Jaynes:
Papers on probability, statistics and statistical physics (E. T.
Jaynes, 1989)
Multivariate Data
Analysis (Fionn Murtagh, 1987)
The History of
Statistics: The Measurement of Uncertainty Before 1900 (Stephen
M. Stigler, 1986)
Statistical
Methods in Experimental Physics (W. A. Eadie et al., 1971)
Method of least
squares and principles of the theory of observations (I. V.
Linnik, 1961)
Combination of
Observations (William M. Smart, 1958)
Statistical
Astronomy (Robert J. Trumpler & Harold F. Weaver, 1953)
Introduction
to
Physical Statistics (Robert
B. Lindsay, 1941)
Scientific
Inference (Harold Jeffreys, 1937)
On the algebraical
and numerical theory of errors of observations and the combination of
observations (George B.
Airy, 1861)
Theory of
the
combination of observations least subject to error (Carl F.
Gauss,
1823, English ed 1995)
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