ORDER STATISTICSThird EditionH. A. DAVID and H. N. NAGARAJA |
WILEY-INTERSCIENCE
ISBN: 0-471-38926-9
wiley.com
Chapters two through nine of this comprehensive volume deal with finite-sample theory, with individual topics grouped under distribution theory (chapters two through six) and statistical inference (chapters seven through nine). Chapters ten and eleven cover asymptotic theory, representing double the coverage of this subject as in the previous edition. Altogether new topics covered include:
Expanded coverage
of short-cut methods, robust estimation, life testing, reliability, L-statistics,
and extreme-value theory complete this one-of-a-kind resource. Students
and researchers of order statistics will appreciate this updated and thorough
edition.
H. N. NAGARAJA,
PhD, is a Professor in the Departments of Statistics and Internal Medicine
at The Ohio State University. He co-edited, along with Pranab K.
Sen and Donald F. Morrison, Statistical Theory and Applications: Papers
in Honor of H. A. David, and coauthored, along with B. C. Arnold and
N. Balakrishnan, Records.
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1
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INTRODUCTION
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1
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1.1
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The
subject of order statistics
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1
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1.2
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The
scope and limits of this book
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3
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1.3
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Notation
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4
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1.4
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Exercises
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7
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2
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BASIC
DISTRIBUTION THEORY
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9
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2.1
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Distribution
of a single order statistic
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9
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2.2
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Joint
distribution of two or more order statistics
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11
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2.3
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Distribution
of the range and of other systematic statistics
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13
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2.4
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Order
statistics for a discrete parent
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16
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2.5
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Conditional
distributions, order statistics as a Markov chain, and independence results
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17
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2.6
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Related
statistics
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20
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2.7
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Exercises
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22
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3
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EXPECTED
VALUES AND MOMENTS
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33
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3.1
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Basic
formulae
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33
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3.2
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Special
continuous distributions
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40
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3.3
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The
discrete case
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42
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3.4
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Recurrence
relations
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44
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3.5
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Exercises
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49
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4
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BOUNDS
AND APPROXIMATIONS FOR MOMENTS OF ORDER STATISTICS
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59
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4.1
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Introduction
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59
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4.2
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Distribution-free
bounds for the moments of order statistics and of the range
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60
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4.3
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Bounds
and approximations by orthogonal inverse expansion
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70
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4.4
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Stochastic
orderings
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74
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4.5
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Bounds
for the expected values of order statistics in terms of quantiles of the
parent distribution
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80
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4.6
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Approximations
to moments in terms of the quantile function and its derivatives
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83
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4.7
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Exercises
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86
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5
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THE
NON-IID CASE
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95
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5.1
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Introduction
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95
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5.2
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Order
statistics for independent nonidentically distributed variates
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96
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5.3
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Order
statistics for dependent variates
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99
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5.4
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Inequalities
and recurrence relations---non-IID cases
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102
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5.5
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Bounds
for linear functions of order statistics and for their expected values
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106
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5.6
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Exercises
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113
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6
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FURTHER
DISTRIBUTION THEORY
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121
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6.1
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Introduction
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121
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6.2
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Studentization
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122
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6.3
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Statistics
expressible as maxima
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124
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6.4
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Random
division of an interval
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133
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6.5
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Linear
functions of order statistics
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137
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6.6
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Moving
order statistics
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140
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6.7
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Characterizations
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142
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6.8
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Concomitants
of order statistics
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144
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6.9
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Exercises
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148
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7
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ORDER
STATISTICS IN NONPARAMETRIC INFERENCE
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159
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7.1
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Distribution-free
confidence intervals for quantiles
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159
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7.2
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Distribution-free
tolerance intervals
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164
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7.3
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Distribution-free
prediction intervals
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167
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7.4
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Exercises
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169
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8
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ORDER
STATISTICS IN PARAMETRIC INFERENCE
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171
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8.1
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Introduction
and basic results
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171
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8.2
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Information
in order statistics
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180
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8.3
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Bootstrap
estimation of quantiles and of moments of order statistics
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183
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8.4
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Least-squares
estimation of location and scale parameters by order statistics
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185
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8.5
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Estimation
of location and scale parameters for censored data
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191
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8.6
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Life
testing, with special emphasis on the exponential distribution
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204
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8.7
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Prediction
of order statistics
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208
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8.8
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Robust
estimation
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211
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8.9
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Exercises
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223
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9
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SHORT-CUT
PROCEDURES
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239
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9.1
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Introduction
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239
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9.2
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Quick
measures of location
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241
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9.3
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Range
and mean range as measures of dispersion
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243
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9.4
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Other
quick measures of dispersion
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248
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9.5
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Quick
estimates in bivariate samples
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250
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9.6
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The
studentized range
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253
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9.7
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Quick
tests
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257
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9.8
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Ranked-set
sampling
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262
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9.9
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O-statistics
and L-moments in data summarization
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268
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9.10
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Probability
plotting and tests of goodness of fit
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270
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9.11
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Statistical
quality control
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274
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9.12
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Exercises
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277
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10
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ASYMPTOTIC
THEORY
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283
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10.1
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Introduction
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283
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10.2
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Representations
for the central sample quantiles
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285
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10.3
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Asymptotic
joint distribution of central quantiles
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288
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10.4
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Optimal
choice of order statistics in large samples
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290
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10.5
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The
asymptotic distribution of the extreme
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296
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10.6
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The
asymptotic joint distribution of extremes
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306
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10.7
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Extreme-value
theory for dependent sequences
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309
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10.8
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Asymptotic
properties of intermediate order statistics
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311
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10.9
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Asymptotic
results for multivariate samples
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313
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10.10
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Exercises
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315
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11
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ASYMPTOTIC
RESULTS FOR FUNCTIONS OF ORDER STATISTICS
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323
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11.1
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Introduction
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323
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11.2
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Asymptotic
distribution of the range, midrange, and spacings
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324
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11.3
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Limit
distribution of the trimmed mean
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329
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11.4
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Asymptotic
normality of linear functions of order statistics
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331
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11.5
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Optimal
asymptotic estimation by order statistics
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335
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11.6
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Estimators
of tail index and extreme quantiles
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341
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11.7
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Asymptotic
theory of concomitants of order statistics
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345
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11.8
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Exercises
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350
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12
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APPENDIX
GUIDE TO TABLES AND ALGORITHMS
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355
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13
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REFERENCES
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367
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14
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INDEX
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451
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