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The k-nearest neighbors method in single index regression model for functional quasi-associated time series data
In the present paper, we consider the k -Nearest Neighbors ( k -NN) method in the single index regression model in the case of a functional predictor...
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Discussion about Properties of First Nearest Neighbor Graphs
AbstractIn this study we present a benchmark of statistical distributions of the first nearest neighbors in random graphs. We consider distribution...
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Nearest Neighbor Sampling of Point Sets Using Rays
We propose a new framework for the sampling, compression, and analysis of distributions of point sets and other geometric objects embedded in...
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Investigation of Statistics of Nearest Neighbor Graphs
AbstractThis paper describes some statistical properties of the nearest neighbor graphs (NNGs). We study the sample distributions of graphs by the...
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Efficient nearest neighbors methods for support vector machines in high dimensional feature spaces
In the context of support vector machines, identifying the support vectors is a key issue when dealing with large data sets. In Camelo et al. (Ann...
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Asymptotics of k-nearest Neighbor Riesz Energies
We obtain new asymptotic results about systems of N particles governed by Riesz interactions involving k -nearest neighbors of each particle as
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Modeling the Nearest Neighbor Graphs to Estimate the Probability of the Independence of Data
AbstractThe proposed method is based on calculations of the statistics of the nearest neighbor graph (NNG) structures, which are presented as a...
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Distances and Nearest Neighbors
At the core of most data analysis tasks and their formulations is a distance. This choice anchors the meaning and the modeling inherent in the...
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Development of Imputation Methods for Missing Data in Multiple Linear Regression Analysis
AbstractMissing data is a common issue in many domains of study. If this issue is disregarded, the erroneous conclusion may be reached. This study’s...
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Survey on KNN Methods in Data Science
The k-nearest neighbors (KNN) algorithm remains a useful and widely applied approach. In the recent years, we have seen many advances in KNN methods,...
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Methods for Compositional Data
You work with compounds of a whole (and, of course, including missing values), for example, measurements of parts per million of chemical elements of...
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Nearest Neighbor Forecasting Using Sparse Data Representation
The method of the nearest neighbors as well as its variants have proven to be very powerful tools in the non-parametric prediction and categorization...
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Modeling the Vibrational Relaxation Rate Using Machine-Learning Methods
AbstractThe aim of the study is to develop an efficient algorithm for solving nonequilibrium gas-dynamics problems in the detailed state-to-state...
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A Machine Learning Approach To Calculating the Non-Equilibrium Diffusion Coefficients in the State-To-State Solution of the Navier–Stokes Equations
AbstractThis work considers the application of machine learning methods for approximate determination of diffusion coefficients that are part of...
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Nonlinear Methods
So far, we have exclusively considered model-free and linear models for regression, since (1) the theory is simple(er), (2) (imputation) models are...
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Linear Methods: Kernels, Variations, and Averaging
In this chapter, we describe linear methods based on kernels or averaging. Principal component analysis (PCA) is a basic method for dimension...
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Distribution-free algorithms for predictive stochastic programming in the presence of streaming data
This paper studies a fusion of concepts from stochastic programming and non-parametric statistical learning in which data is available in the form of...
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General Considerations on Univariate Methods: Single and Multiple Imputation
Missing or invalid values clearly affect the quality of data analysis, model results, and classification performance. However, the methods and...
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Prediction of Maneuvering Status for Aerial Vehicles Using Supervised Learning Methods
Aerial vehicles follow a guided approach based on Latitude, Longitude, and Altitude. This information can be used for calculating the status of...
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Jobs Runtime Forecast for JSCC RAS Supercomputers Using Machine Learning Methods
AbstractThe paper is devoted to machine learning methods and algorithms for the supercomputer jobs execution prediction. The supercomputers...
