In the problem of binary classification (or medical diagnosis), the classification rule (or
diagnostic test) produces a continuous decision variable which is compared to a critical
value (or threshold). Test values above (or below) that threshold are called positive (or
negative) for disease. The two types of errors associated with every threshold value are
Type I (false positive) and Type II (false negative) errors.
This book presents a systematic and unified approach for modern <em>nonparametric</em> treatment of missing and modified data via examples of density and hazard rate <em>estimation</em>, <em>nonparametric</em> regression, filtering signals, and time series analysis. All basic types of missing at random and not at random, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications.
The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study.
The book is primarily about practically important small samples. It explains when consistent <em>estimation</em> is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent <em>estimation</em> impossible, then the author explains what type of action is needed to restore the lost information.
The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively.
Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of <em>nonparametric</em> curve <em>estimation</em> and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association.
I'm an absolute beginner in C++ and I'm doing my third year project on Image blending in Wavelet domain. I've got no idea which C++ environment will be the best one to choose from and which classes I need to use to complete this project. Could experts give me some tips and advises. Thank you very much.
An algorithm based on <em>wavelet</em> transforms (WT's)
has been developed for detecting ECG characteristic points.
With the multiscale feature of WT's, the QRS complex can be
distinguished from high P or T waves, noise, baseline drift,
and artifacts. The relation between the characteristic points of
ECG signal and those of modulus maximum pairs of its WT’s
is illustrated. By <em>using</em> this method, the detection rate of QRS
complexes is above 99.8% for the MIT/BIH database and the
P and T waves can also be detected, even with serious baseline
drift and noise.
The process of classifying objects is a fundamental fea-
ture of most human pursuits, and the idea that people clas-
sify together those things that people ¯nd similar is both
intuitive and popular across a wide range of disciplines. Es-
timation of di®erence between curves (curve matching) is
an useful and often necessary technique in many applica-
tions, including: pattern recognition, image object recogni-
tion, robotic applications, computational geometry, etc.
In this paper, three methods for curve matching <em>using</em>
turning functions are presented. While the ¯rst two, called
plain and polygonal method, are based on a simple adapta-
tion of the existing approaches, the third one, called penalty
method, is a new one and tries to overcome some important
problems from the ¯rst two. The advantages and essential
problems of the proposed methods are also discussed. A
number of examples are presented to show major di®erences
among the methods and their potential usefulness.
This paper considers the problem of automatic <em>estimation</em> of crowd densities, an important part of the problem of automatic crowd monitoring and control. A new technique based on texture description of the images of the area under surveillance is proposed. Two methods based on different approaches of texture analysis, one statistical and another spectral, are applied on real images captured in an area of Liverpool Street Railway Station, London, UK. The results obtained show that both methods present similar general rates of correct <em>estimation</em>, and that the potential use of texture description for the problem of automatic <em>estimation</em> of crowd densities is encouraging.
abstract: We consider the problem of broadcasting in an ad hoc
wireless <em>network</em>, where all nodes of the <em>network</em> are sources that
want to transmit information to all other nodes. Our figure of merit
is energy efficiency, a critical design parameter for wireless <em>network</em>s
since it directly affects battery life and thus <em>network</em> lifetime.
We prove that applying ideas from <em>network</em> coding allows
to realize significant benefits in terms of energy efficiency for the
problem of broadcasting, and propose very simple algorithms that
allow to realize these benefits in practice. In particular, our theoretical
analysis shows that <em>network</em> coding improves performance
by a constant factor in fixed <em>network</em>s. We calculate this factor
exactly for some canonical configurations. We then show that in
<em>network</em>s where the topology dynamically changes, for example
due to mobility, and where operations are restricted to simple distributed
algorithms, <em>network</em> coding can offer improvements of a
with Applications to
Science and Engineering
Paul H. Kvam
2 Probability Basics
3 Statistics Basics
4 Bayesian Statistics
5 Order Statistics
6 Goodness of Fit
7 Rank Tests
8 Designed Experiments
9 Categorical Data
10 Estimating Distribution Functions
11 Density Estimation
12 Beyond Linear Regression
13 Curve Fitting Techniques
16 EM Algorithm
17 Statistical Learning
18 Nonparametric Bayes
This book is the first systematic treatment of Bayesian <em>nonparametric</em> methods and the theory behind them. It will also appeal to statisticians in general. The book is primarily aimed at graduate students and can be used as the text for a graduate course in Bayesian non-parametrics.
This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in <em>nonparametric</em> inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern <em>nonparametric</em> methods. It covers a wide range of topics including the bootstrap, the <em>nonparametric</em> delta method, <em>nonparametric</em> regression, density <em>estimation</em>, orthogonal function methods, minimax <em>estimation</em>, <em>nonparametric</em> confidence sets, and <em>wavelet</em>s. The book’s dual approach includes a mixture of methodology and theory.
The theory and methods of smoothing have been developed mainly in the last ten years.
The intensive interest in smoothing over this last decade had two reasons: statisticians
realized that pure parametric thinking in curve <em>estimation</em>s often does not meet the need for
flexibility in data analysis and the development of hardware created the demand for theory
of now computable <em>nonparametric</em> estimates.
This book is the fruit of recent advances concerning both <em>nonparametric</em> statistical
modelling and functional variables,presents in a original way new <em>nonparametric</em> statistical methods for functional data analysis.
Engineering disciplines are those fields of research and development that
attempt to create products and systems operating in, and dealing with,
the real world. The number of disciplines is large, as is the range of scales
that they typically operate in: from the very small scale of nanotechnology
up to very large scales that span whole regions, e.g. water management
systems, electric power distribution systems, or even global systems
(e.g. the global positioning system, GPS). The level of advancement in
the fields also varies wildly, from emerging techniques (again, nanotechnology)
to trusted techniques that have been applied for centuries (architecture,
hydraulic works). Nonetheless, the disciplines share one
important aspect: engineering aims at designing and manufacturing
systems that interface with the world around them.
This book will be a massive ally in troubleshooting your <em>network</em> <em>using</em> Wireshark, the world's most popular analyzer. Over 100 practical recipes provide a focus on real-life situations, helping you resolve your own individual issues.
In this paper, we discuss the use of Social Network Analysis in analysing such organisations in order to make recommendations about work practices, and to
suggest new placements of staff within a physical headquarters setting.
Over 100 recipes to analyze and troubleshoot <em>network</em> problems <em>using</em> Wireshark 2
This book contains practical recipes on troubleshooting a data communications <em>network</em>. This second version of the book focuses on Wireshark 2, which has already gained a lot of traction due to the enhanced features that it offers to users. The book expands on some of the subjects explored in the first version, including TCP performance, <em>network</em> security, Wireless LAN, and how to use Wireshark for cloud and virtual system monitoring. You will learn how to analyze end-to-end IPv4 and IPv6 connectivity failures for Unicast and Multicast traffic <em>using</em> Wireshark. It also includes Wireshark capture files so that you can practice what you’ve learned in the book. You will understand the normal operation of E-mail protocols and learn how to use Wireshark for basic analysis and troubleshooting. Using Wireshark, you will be able to resolve and troubleshoot common applications that are used in an enterprise <em>network</em>, like NetBIOS and SMB protocols. Finally, you will also be able to measure <em>network</em> parameters, check for <em>network</em> problems caused by them, and solve them effectively. By the end of this book, you’ll know how to analyze traffic, find patterns of various offending traffic, and secure your <em>network</em> from them.
What You Will Learn
Configure Wireshark 2 for effective <em>network</em> analysis and troubleshooting
Set up various display and capture filters
Understand <em>network</em>ing layers, including IPv4 and IPv6 analysis
Explore performance issues in TCP/IP
Get to know about Wi-Fi testing and how to resolve problems related to wireless LANs
Get information about <em>network</em> phenomena, events, and errors
Locate faults in detecting security failures and breaches in <em>network</em>s
DescriptionnnThe discrete <em>wavelet</em> transform is a popular tool for signal compression. In this problem, your job is to write a program to decompress a one-dimensional signal (a list of integers) that has been compressed by a simple <em>wavelet</em> transform.nnTo understand how this simple <em>wavelet</em> transform works, suppose that we have a list of an even number of integers. We compute the sum and difference of each pair of consecutive samples, resulting in two lists of sums and differences each having half the original length. Formally, if the original samples arenna(1),..., a(n)nthe i-th sum s(i) and difference d(i) are computed as:nfor i = 1,...,n/2:nn s(i) = a(2*i-1) + a(2*i)nn d(i) = a(2*i-1) - a(2*i)nThis is then rearranged to give the transformed signal by first listing the sums and then the differences. For example, if the input signal is:n 5, 2, 3, 2, 5, 7, 9, 6nThen the sum and difference signals are:n s(i) = 7, 5, 12, 15nn d(i) = 3, 1, -2, 3nThus, the transformed signal is:n 7, 5, 12, 15, 3, 1, -2, 3nThe same process is applied recursively to the first half of the transformed signal, treating s(i) as the input signal, until the length of the input signal is 1. In the example above, the final transformed signal is:nn 39, -15, 2, -3, 3, 1, -2, 3nIt is assumed that the length of the original input is a power of 2, and the input signal consists of integers between 0 and 255 (inclusive) only.nInputnnThe input consists of a number of cases. Each case is specified on a line, starting with an integer N (1 <= 256) indicating the number of samples. The next N integers are the transformed samples. The end of input is indicated by a case in which N = 0.nOutputnnFor each test case, output the original samples on a single line, separated by a single space.nSample Inputnn8 39 -15 2 -3 3 1 -2 3n4 10 -4 -1 -1n0nSample Outputnn5 2 3 2 5 7 9 6n1 2 3 4
Quad-quaternion MUSIC for DOA Estimation Using Electromagnetic Vector-Sensors，龚晓峰，徐友根，A new quad-quaternion model is herein established for a six-component electro-magnetic vector-sensor array, under which a multidimensional-algebra based direc-tion-of-arrival (DOA)
Covariance <em>estimation</em> for high dimensional vectors is a classically difcult problem
in statistical analysis and machine learning. In this paper, we propose a
maximum likelihood (ML) approach to covariance <em>estimation</em>, which employs a
novel sparsity constraint. More specically, the covariance is constrained to have
an eigen decomposition which can be represented as a sparse matrix transform
(SMT). The SMT is formed by a product of pairwise coordinate rotations known
as Givens rotations. Using this framework, the covariance can be efciently estimated
<em>using</em> greedy minimization of the log likelihood function, and the number
of Givens rotations can be efciently computed <em>using</em> a cross-validation procedure.
The resulting estimator is positive denite and well-conditioned even when
the sample size is limited. Experiments on standard hyperspectral data sets show
that the SMT covariance estimate is consistently more accurate than both traditional
shrinkage estimates and recently proposed graphical lasso estimates for a
variety of different classes and sample sizes.
Action recognition and human pose <em>estimation</em> are
closely related but both problems are generally handled
as distinct tasks in the literature. In this work, we pro-
pose a multitask framework for jointly 2D and 3D pose
<em>estimation</em> from still images and human action recogni-
tion from video sequences. We show that a single archi-
tecture can be used to solve the two problems in an effi-
cient way and still achieves state-of-the-art results. Ad-
ditionally, we demonstrate that optimization from end-to-
end leads to significantly higher accuracy than separated
learning. The proposed architecture can be trained with
data from different categories simultaneously in a seam-
lessly way. The reported results on four datasets (MPII,
Human3.6M, Penn Action and NTU) demonstrate the effec-
tiveness of our method on the targeted tasks.