117,610 research outputs found
Missing at random, likelihood ignorability and model completeness
This paper provides further insight into the key concept of missing at random
(MAR) in incomplete data analysis. Following the usual selection modelling
approach we envisage two models with separable parameters: a model for the
response of interest and a model for the missing data mechanism
(MDM). If the response model is given by a complete density family, then
frequentist inference from the likelihood function ignoring the MDM is valid if
and only if the MDM is MAR. This necessary and sufficient condition also holds
more generally for models for coarse data, such as censoring.
Examples are given to show the necessity of the completeness of the
underlying model for this equivalence to hold
Collaborative signal and information processing for target detection with heterogeneous sensor networks
In this paper, an approach for target detection and acquisition with heterogeneous sensor networks through strategic resource allocation and coordination is presented. Based on sensor management and collaborative signal and information processing, low-capacity low-cost sensors are strategically deployed to guide and cue scarce high performance sensors in the network to improve the data quality, with which the mission is eventually completed more efficiently with lower cost. We focus on the problem of designing such a network system in which issues of resource selection and allocation, system behaviour and capacity, target behaviour and patterns, the environment, and multiple constraints such as the cost must be addressed simultaneously. Simulation results offer significant insight into sensor selection and network operation, and demonstrate the great benefits introduced by guided search in an application of hunting down and capturing hostile vehicles on the battlefield
Applications of concurrent access patterns in web usage mining
This paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access pat-terns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation
XML data integrity based on concatenated hash function
Data integrity is the fundamental for data authentication. A major problem for XML data authentication is that signed XML data can be copied to another document but still keep signature valid. This is caused by XML data integrity protecting. Through investigation, the paper discovered that besides data content integrity, XML data integrity should also protect element location information, and context referential integrity under fine-grained security situation. The aim of this paper is to propose a model for XML data integrity considering XML data features. The paper presents an XML data integrity model named as CSR (content integrity, structure integrity, context referential integrity) based on a concatenated hash function. XML data content integrity is ensured using an iterative hash process, structure integrity is protected by hashing an absolute path string from root node, and context referential integrity is ensured by protecting context-related elements. Presented XML data integrity model can satisfy integrity requirements under situation of fine-grained security, and compatible with XML signature. Through evaluation, the integrity model presented has a higher efficiency on digest value-generation than the Merkle hash tree-based integrity model for XML data
Array signal processing for maximum likelihood direction-of-arrival estimation
Emitter Direction-of-Arrival (DOA) estimation is a fundamental problem in a variety of applications including radar, sonar, and wireless communications. The research has received considerable attention in literature and numerous methods have been proposed. Maximum Likelihood (ML) is a nearly optimal technique producing superior estimates compared to other methods especially in unfavourable conditions, and thus is of significant practical interest. This paper discusses in details the techniques for ML DOA estimation in either white Gaussian noise or unknown noise environment. Their performances are analysed and compared, and evaluated against the theoretical lower bounds
On the third critical field in Ginzburg-Landau theory
Using recent results by the authors on the spectral asymptotics of the
Neumann Laplacian with magnetic field, we give precise estimates on the
critical field, , describing the appearance of superconductivity in
superconductors of type II. Furthermore, we prove that the local and global
definitions of this field coincide. Near only a small part, near the
boundary points where the curvature is maximal, of the sample carries
superconductivity. We give precise estimates on the size of this zone and decay
estimates in both the normal (to the boundary) and parallel variables
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