Search
Search Results
-
A Novel Classification Algorithm Based on the Synergy Between Dynamic Clustering with Adaptive Distances and K-Nearest Neighbors
This paper introduces a novel supervised classification method based on dynamic clustering (DC) and K-nearest neighbor (KNN) learning algorithms,...
-
A p-step-ahead sequential adaptive algorithm for D-optimal nonlinear regression design
Under a nonlinear regression model with univariate response an algorithm for the generation of sequential adaptive designs is studied. At each stage,...
-
Evaluating the adequacy of variance function using pairwise distances
In this article, we develop a distance-based testing for assessing the adequacy of conditional variance function using pairwise distances. Under the...
-
Statistical Analysis of Improved Type-II Adaptive Progressive Hybrid Censored NH Data
Recently, improved Type-II adaptive progressive censoring has been introduced to ensure that the experimental duration does not exceed a certain time...
-
Inference of improved adaptive progressively censored competing risks data for Weibull lifetime models
Recently, an improved adaptive Type-II progressive censoring scheme is proposed to ensure that the experimental time will not pass a pre-fixed time...
-
Nonparametric tests for combined location-scale and Lehmann alternatives using adaptive approach and max-type metric
The paper deals with the classical two-sample problem for the combined location-scale and Lehmann alternatives, known as the versatile alternative....
-
Autonomous adaptive control of manufacturing parameters based on local regression modeling
The demand for the autonomous adaptive control of manufacturing lines has been growing to realize productivity improvement and carbon neutrality. We...
-
An adaptive method for bandwidth selection in circular kernel density estimation
Kernel density estimations of circular data are an effective type of nonparametric estimation. The performance of these estimations depends...
-
More efficient fully Bayesian adaptive testing with a revised proposal distribution
The present study improved the efficiency of the fully Bayesian algorithm for adaptive testing proposed in van der Linden and Ren (2020) by revising...
-
Plateau proposal distributions for adaptive component-wise multiple-try metropolis
Markov chain Monte Carlo (MCMC) methods are sampling methods that have become a commonly used tool in statistics, for example to perform Monte Carlo...
-
Computing T-optimal designs via nested semi-infinite programming and twofold adaptive discretization
Modelling real processes often results in several suitable models. In order to be able to distinguish, or discriminate, which model best represents a...
-
Uniform in Bandwidth Consistency of Conditional U-statistics Adaptive to Intrinsic Dimension in Presence of Censored Data
U -statistics represent a fundamental class of statistics from modelling quantities of interest defined by multi-subject responses. U -statistics...
-
Adaptive Minimax Testing for Circular Convolution
AbstractGiven observations from a circular random variable contaminated by an additive measurement error, we consider the problem of minimax optimal...
-
A further study comparing forward search multivariate outlier methods including ATLA with an application to clustering
This paper makes comparisons of automated procedures for robust multivariate outlier detection through discussion and simulation. In particular,...
-
Constrained clustering and multiple kernel learning without pairwise constraint relaxation
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics...
-
Standard GEE Modeling of Correlated Univariate Outcomes
Formulations are provided for correlated sets of univariate outcomes allowing for missing values, generalized linear modeling of means for such...
-
Batch Self-Organizing Maps for Distributional Data with an Automatic Weighting of Variables and Components
This paper deals with a batch self organizing map algorithm for data described by distributional-valued variables (DBSOM). Such variables are...
-
Deep Spatial Q-Learning for Infectious Disease Control
Infectious diseases are a cause of humanitarian and economic crises across the world. In develo** regions, a severe epidemic can result in the...
-
Adaptive iterative Hessian sketch via A-optimal subsampling
Iterative Hessian sketch (IHS) is an effective sketching method for modeling large-scale data. It was originally proposed by Pilanci and Wainwright...
-
Diffusion-Weighted Imaging
Diffusion -weighted Magnetic Resonance Imaging (dMRI) has long proven to be a versatile tool for the in-vivo microstructural investigation of the...