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Overlap** coefficient in network-based semi-supervised clustering
Network-based Semi-Supervised Clustering (NeSSC) is a semi-supervised approach for clustering in the presence of an outcome variable. It uses a...
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U-type Tube Vibration Based Fuel Density Portable Testing Instrument Research
To overcome the shortcomings of traditional engine fuel testing equipment such as poor portability, poor stability, and expensive, a portable fuel...
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High-cardinality categorical covariates in network regressions
High-cardinality (nominal) categorical covariates are challenging in regression modeling, because they lead to high-dimensional models. For example,...
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Models of Network Delay
In this paper several mathematical models for end-to-end network delay are derived, where exponential wait times at intermediate network routers are...
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Mixture modeling with normalizing flows for spherical density estimation
Normalizing flows are objects used for modeling complicated probability density functions, and have attracted considerable interest in recent years....
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An attribute-based Node2Vec model for dynamic community detection on co-authorship network
Networks offer a wide range of applications in various domains of life and scientific research. Community detection, which aims at understanding the...
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Pointwise density estimation on metric spaces and applications in seismology
We are studying the problem of estimating density in a wide range of metric spaces, including the Euclidean space, the sphere, the ball, and various...
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Stacking-based neural network for nonlinear time series analysis
Stacked generalization is a commonly used technique for improving predictive accuracy by combining less expressive models using a high-level model....
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On the Randić index and its variants of network data
Summary statistics play an important role in network data analysis. They can provide us with meaningful insight into the structure of a network. The...
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Network-Based Discriminant Analysis for Multiclassification
Classification for multi-label responses, known as multiclassification, has been an important problem in supervised learning and has attracted our...
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Density Estimation by Monte Carlo and Quasi-Monte Carlo
Estimating the density of a continuous random variable X has been studied extensively in statistics, in the setting where n independent observations...
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Estimation of the Parameters in an Expanding Dynamic Network Model
In this paper, we consider an expanding sparse dynamic network model where the time evolution is governed by a Markovian structure. Transition of the...
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A dynamic network model to measure exposure concentration in the Austrian interbank market
Motivated by an original financial network dataset, we develop a statistical methodology to study non-negatively weighted temporal networks. We focus...
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Network-Based Dimensionality Reduction for Textual Datasets
There is an increasing interest in develo** statistical tools for extracting information from textual datasets. In a text mining framework, a...
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Using Density and Fuzzy Clustering for Data Cleaning and Segmental Description of Livestock Data
The cluster algorithms density-based clustering with noise and fuzzy c-means were used to edit and group a large, noisy data set from a livestock...
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Bayesian Network Model
This chapter describes the Bayesian network model (BNM; e.g., Pearl, 1988; Jensen & Nielsen, 2007; Darwiche, 2009; Koski & Noble, 2009; Ueno, 2013;...
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Online network monitoring
An important problem in network analysis is the online detection of anomalous behaviour. In this paper, we introduce a network surveillance method...
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Bicluster Network Model
This chapter introduces the bicluster network model (BINET; Shojima, 2019), a combination of the Bayesian network model (BNM; Chap. 8...
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Classifying for images based on the extracted probability density function and the quasi Bayesian method
This study presents a novel algorithm for image classification based on a quasi-Bayesian approach and the extraction of probability density functions...
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Density estimation via Bayesian inference engines
We explain how effective automatic probability density function estimates can be constructed using contemporary Bayesian inference engines such as...
