搜索结果: 1-14 共查到“统计学 Detecting”相关记录14条 . 查询时间(0.078 秒)
Transient phenomena are interesting and potentially highly revealing of details about the processes under observation and study that could otherwise go unnoticed. It is therefore important to maximise...
Detecting Overlapping Temporal Community Structure in Time-Evolving Networks
Detecting Overlapping Temporal Community Structure Time-Evolving Networks
2013/5/2
We present a principled approach for detecting overlapping temporal community structure in dynamic networks. Our method is based on the following framework: find the overlapping temporal community str...
Detecting Events and Patterns in Large-Scale User Generated Textual Streams with Statistical Learning Methods
Detecting Events Patterns Large-Scale User Generated Textual Streams Statistical Learning Methods
2012/9/18
A vast amount of textual web streams is influenced by events or phenomena emerging in the real world. The social web forms an excellent modern paradigm, where unstructured user generated content is pu...
Detecting sparse cone alternatives for Gaussian random fields, with an application to fMRI
random elds Euler characteristic kinematic formulae volumes of tubes expansion order-restricted inference multivari-ate one-sided hypotheses non-negative least squares.
2012/9/19
Our problem is to nd a good approximation to the P-value of the maximum of a random eld of test statistics for a cone alternative at each point in a sample of Gaussian random elds. These test stati...
Detecting and handling outlying trajectories in irregularly sampled functional datasets
outlying trajectories sampled functional datasets
2010/11/8
Outlying curves often occur in functional or longitudinal datasets,and can be very influential on parameter estimators and very hard to detect visually. In this article we introduce estimators of the ...
Semi-parametric dynamic time series modelling with applications to detecting neural dynamics
Dynamic time series modeling change-point testing Bayesian statistics statistics for neural data
2010/11/8
This paper illustrates novel methods for nonstationary time se-ries modeling along with their applications to selected problems in neuroscience. These methods are semi-parametric in that inferences ar...
Detecting Weak but Hierarchically-Structured Patterns in Networks
Weak Hierarchically-Structured Patterns Networks
2010/3/11
The ability to detect weak distributed activation patterns in networks is critical to several applications, such as identifying the onset of anomalous activity or incipient congestion in the Internet,...
Weighted Dickey-Fuller Processes for Detecting Stationarity
Autoregressive unit root change point control chart nonparametric smooth-ing sequential analysis robustness
2010/3/9
Aiming at monitoring a time series to detect stationarity as soon as possible,
we introduce monitoring procedures based on kernel-weighted sequential Dickey-Fuller
(DF) processes, and related stoppi...
Bayesian Diagnostic Techniques for Detecting Hierarchical Structure
model assessment partial posterior predictive p value posterior predictive distribution posterior predictive p value
2009/9/22
Motivated by an increasing number of Bayesian hierarchical model
applications, the objective of this paper is to evaluate properties of several di-
agnostic techniques when the tted model includes s...
Detecting Non-Identifiability on the Poly-Weibull Model
Non-Identifiability the Poly-Weibull Model
2009/9/18
Detecting Non-Identifiability on the Poly-Weibull Model。
Innovated Higher Criticism for detecting sparse signals in correlated noise
Adding noise Cholesky factorization empirical process innovation multiplehypothesis testing sparse normal means
2010/3/18
Higher Criticism is a method for detecting signals that are both sparse and weak. Although
first proposed in cases where the noise variables are independent, Higher Criticism
also has reasonable per...
Detecting a conditional extrme value model
Regular variation domain of attraction heavy tails asymptotic independence conditional extremevalue model
2010/3/18
In classical extreme value theory probabilities of extreme events are estimated assuming all
the components of a random vector to be in a domain of attraction of an extreme value distribution. In con...
Detecting spatial patterns with the cumulant function. Part I：The theory
Pattern Analysis Cumulant Function Multivariate Gaussian Skew-normal and Gamma vectors
2010/4/30
In climate studies, detecting spatial patterns that
largely deviate from the sample mean still remains a statistical
challenge. Although a Principal Component Analysis
(PCA), or equivalently a Empi...
Event Weighted Tests for Detecting Periodicity in Photon Arrival Times
Event Weighted Tests Detecting Periodicity Photon Arrival Times
2010/4/29
This paper treats the problem of detecting periodicity in a sequence of photon arrival times,
which occurs, for example, in attempting to detect gamma-ray pulsars. A particular focus is on
how auxil...