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1.

図書
図書
1. Detection theory
Steven M. Kay
出版情報: Englewood Cliffs ; Upper Saddle River, N.J. : PTR Prentice-Hall, c1998  xiv, 560 p. ; 25 cm
シリーズ名: Prentice Hall signal processing series ; . Fundamentals of statistical signal processing ; v. 2
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目次情報: 続きを見る
Introduction / 1：
Detection Theory in Signal Processing
The Detection Problem
The Mathematical Detection Problem
Hierarchy of Detection Problems
Role of Asymptotics
Some Notes to the Reader
Summary of Important PDFs / 2：
Fundamental Probability Density Functionshfil Penalty - M and Properties
Quadratic Forms of Gaussian Random Variables
Asymptotic Gaussian PDF
Monte Carlo Performance Evaluation
Number of Required Monte Carlo Trials
Normal Probability Paper
MATLAB Program to Compute Gaussian Right-Tail Probability and its Inverse
MATLAB Program to Compute Central and Noncentral c 2 Right-Tail Probability
MATLAB Program for Monte Carlo Computer Simulation
Statistical Decision Theory I / 3：
Neyman-Pearson Theorem
Receiver Operating Characteristics
Irrelevant Data
Minimum Probability of Error
Bayes Risk
Multiple Hypothesis Testing
Minimum Bayes Risk Detector - Binary Hypothesis
Minimum Bayes Risk Detector - Multiple Hypotheses
Deterministic Signals / 4：
Matched Filters
Generalized Matched Filters
Multiple Signals
Linear Model
Signal Processing Examples
Reduced Form of the Linear Model1
Random Signals / 5：
Estimator-Correlator
Linear Model1
Estimator-Correlator for Large Data Records
General Gaussian Detection
Signal Processing Example
Detection Performance of the Estimator-Correlator
Statistical Decision Theory II / 6：
Composite Hypothesis Testing
Composite Hypothesis Testing Approaches
Performance of GLRT for Large Data Records
Equivalent Large Data Records Tests
Locally Most Powerful Detectors
Asymptotically Equivalent Tests - No Nuisance Parameters
Asymptotically Equivalent Tests - Nuisance Parameters
Asymptotic PDF of GLRT
Asymptotic Detection Performance of LMP Test
Alternate Derivation of Locally Most Powerful Test
Derivation of Generalized ML Rule
Deterministic Signals with Unknown Parameters / 7：
Signal Modeling and Detection Performance
Unknown Amplitude
Unknown Arrival Time
Sinusoidal Detection
Classical Linear Model
Asymptotic Performance of the Energy Detector
Derivation of GLRT for Classical Linear Model
Random Signals with Unknown Parameters / 8：
Incompletely Known Signal Covariance
Large Data Record Approximations
Weak Signal Detection
Derivation of PDF for Periodic Gaussian Random Process
Unknown Noise Parameters / 9：
General Considerations
White Gaussian Noise
Colored WSS Gaussian Noise
Derivation of GLRT for Classical Linear Model for s 2 Unknown
Rao Test for General Linear Model with Unknown Noise Parameters
Asymptotically Equivalent Rao Test for Signal Processing Example
NonGaussian Noise / 10：
NonGaussian Noise Characteristics
Known Deterministic Signals
Asymptotic Performance of NP Detector for Weak Signals
BRao Test for Linear Model Signal with IID NonGaussian Noise
Summary of Detectors / 11：
Detection Approaches
Choosing a Detector
Other Approaches and Other Texts
Model Change Detection / 12：
Description of Problem
Extensions to the Basic Problem
Multiple Change Times
General Dynamic Programming Approach to Segmentation
MATLAB Program for Dynamic Programming
Complex/Vector Extensions, and Array Processing / 13：
Known PDFs
PDFs with Unknown Parameters
Detectors for Vector Observations
Introduction / 1：
Detection Theory in Signal Processing
The Detection Problem
2.

図書
図書
2. The EM algorithm and extensions
Geoffrey J. McLachlan, Thriyambakam Krishnan
出版情報: New York : John Wiley, c1997  xvii, 274 p. ; 24 cm
シリーズ名: Wiley series in probability and mathematical statistics ; . Applied probability and statistics
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目次情報: 続きを見る
Preface to the Second Edition
Preface to the First Edition
List of Examples
General Introduction. / 1：
Introduction / 1.1：
Maximum Likelihood Estimation / 1.2：
Newton-Type Methods / 1.3：
Introductory Examples / 1.4：
Formulation of the EM Algorithm / 1.5：
EM Algorithm for MAP and MPL Estimation / 1.6：
Brief Summary of the Properties of EM Algorithm / 1.7：
History of the EM Algorithm / 1.8：
Overview of the Book / 1.9：
Notations / 1.10：
Examples of the EM Algorithm. / 2：
Multivariate Data with Missing Values / 2.1：
Least Square with the Missing Data / 2.3：
Example 2.4: Multinomial with Complex Cell Structure / 2.4：
Example 2.5: Analysis of PET and SPECT Data / 2.5：
Example 2.6: Multivariate t-Distribution / Known D.F.2.6：
Finite Normal Mixtures / 2.7：
Example 2.9: Grouped and Truncated Data / 2.8：
Example 2.10: A Hidden Markov AR(1) Model / 2.9：
Basic Theory of the EM Algorithm. / 3：
Monotonicity of a Generalized EM Algorithm / 3.1：
Convergence of an EM Sequence to a Stationary Value / 3.3：
Convergence of an EM Sequence of Iterates / 3.5：
Examples of Nontypical Behavior of an EM (GEM) Sequence / 3.6：
Score Statistic / 3.7：
Missing Information / 3.8：
Rate of Convergence of the EM Algorithm / 3.9：
Standard Errors and Speeding up Convergence. / 4：
Observed Information Matrix / 4.1：
Approximations to Observed Information Matrix: i.i.d. Case / 4.3：
Observed Information Matrix for Grouped Data / 4.4：
Supplemented EM Algorithm / 4.5：
Bookstrap Approach to Standard Error Approximation / 4.6：
BakerÆs, LouisÆ, and OakesÆ Methods for Standard Error Computation / 4.7：
Acceleration of the EM Algorithm via AitkenÆs Method / 4.8：
An Aitken Acceleration-Based Stopping Criterion / 4.9：
conjugate Gradient Acceleration of EM Algorithm / 4.10：
Hybrid Methods for Finding the MLE / 4.11：
A GEM Algorithm Based on One Newton-Raphson Algorithm / 4.12：
EM gradient Algorithm / 4.13：
A Quasi-Newton Acceleration of the EM Algorithm / 4.14：
Ikeda Acceleration / 4.15：
Extension of the EM Algorithm / 5：
ECM Algorithm / 5.1：
Multicycle ECM Algorithm / 5.3：
Example 5.2: Normal Mixtures with Equal Correlations / 5.4：
Example 5.3: Mixture Models for Survival Data / 5.5：
Example 5.4: Contingency Tables with Incomplete Data / 5.6：
ECME Algorithm / 5.7：
Example 5.5: MLE of t-Distribution with the Unknown D.F / 5.8：
Example 5.6: Variance Components / 5.9：
Linear Mixed Models / 5.10：
Example 5.8: Factor Analysis / 5.11：
Efficient Data Augmentation / 5.12：
Alternating ECM Algorithm / 5.13：
Example 5.9: Mixtures of Factor Analyzers / 5.14：
Parameter-Expanded EM (PX-EM) Algorithm / 5.15：
EMS Algorithm / 5.16：
One-Step-Late Algorithm / 5.17：
Variance Estimation for Penalized EM and OSL Algorithms / 5.18：
Incremental EM / 5.19：
Linear Inverse problems / 5.20：
Monte Carlo Versions of the EM Algorithm. / 6：
Monte Carlo Techniques / 6.1：
Monte Carlo EM / 6.3：
Data Augmentation / 6.4：
Bayesian EM / 6.5：
I.I.D. Monte Carlo Algorithm / 6.6：
Markov Chain Monte Carlo Algorithms / 6.7：
Gibbs Sampling / 6.8：
Examples of MCMC Algorithms / 6.9：
Relationship of EM to Gibbs Sampling / 6.10：
Data Augmentation and Gibbs Sampling / 6.11：
Empirical Bayes and EM / 6.12：
Multiple Imputation / 6.13：
Missing-Data Mechanism, Ignorability, and EM Algorithm / 6.14：
Some Generalization of the EM Algorithm. / 7：
Estimating Equations and Estimating Functions / 7.1：
Quasi-Score and the Projection-Solution Algorithm / 7.3：
Expectation-Solution (ES) Algorithm / 7.4：
Other Generalization / 7.5：
Variational Bayesian EM Algorithm / 7.6：
MM Algorithm / 7.7：
Lower Bound Maximization / 7.8：
Interval EM Algorithm / 7.9：
Competing Methods and Some Comparisons with EM / 7.10：
The Delta Algorithm / 7.11：
Image Space Reconstruction Algorithm / 7.12：
Further Applications of the EM Algorithm. / 8：
Hidden Markov Models / 8.1：
AIDS Epidemiology / 8.3：
Neural Networks / 8.4：
Data Mining / 8.5：
Bioinformatics / 8.6：
References
Author Index
Subject Index
Preface to the Second Edition
Preface to the First Edition
List of Examples
3.

図書
図書
3. System identification : parameter and state estimation
Pieter Eykhoff
出版情報: London ; Chichester : John Wiley & Sons, c1974  xx, 555 p. ; 24 cm
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4.

図書
図書
4. Random signals, estimation, and identification : analysis and applications
Nirode Mohanty
出版情報: New York : Van Nostrand Reinhold, c1986  xii, 626 p. ; 24 cm
シリーズ名: Van Nostrand Reinhold electrical/computer science and engineering series
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5.

図書
図書
5. Estimating the parameters of the Markov probability model from aggregate time series data. 2nd, rev. ed
T.C. Lee, G.G. Judge, A. Zellner
出版情報: Amsterdam : North-Holland , New York : Elsevier North-Holland, distributor, 1977  260 p. ; 23 cm
シリーズ名: Contributions to economic analysis ; 65
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6.

図書
図書
6. Kalman filtering theory (pbk.)
A.V. Balakrishnan
出版情報: New York : Optimization Software, Inc., Publications Division, c1984  xii, 222 p. ; 24 cm
シリーズ名: University series in modern engineering
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7.

図書
図書
7. Parametric estimation
[by] M. T. Wasan
出版情報: New York : McGraw-Hill, c1970  xiv, 256 p ; 24 cm
シリーズ名: McGraw-Hill series in probability and statistics / David Blackwell and Herbert Solomon, consulting editors
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8.

図書
図書
8. Nonlinear estimation (: us ; : gw)
Gavin J.S. Ross
出版情報: New York ; Tokyo : Springer-Verlag, c1990  viii, 189 p. ; 25 cm
シリーズ名: Springer series in statistics
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9.

図書
図書
9. An introduction to signal detection and estimation
H. Vincent Poor
出版情報: New York : Springer-Verlag, c1988  x, 549 p. ; 24 cm
シリーズ名: Springer texts in electrical engineering
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10.

図書
図書
10. Transformation and weighting in regression
Raymond J. Carroll and David Ruppert
出版情報: New York : Chapman and Hall, 1988  x, 249 p. ; 22 cm
シリーズ名: Monographs on statistics and applied probability
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