28 papers · ranked by Valyu relevance
Guiqin Liang, Jian Zhang, Dave Winkler
An accurate classification of material stability often requires fusing multiple features under uncertainty. Dempster-Shafer (D-S) theory is a powerful framework for multi-source information fusion under uncertainty. However, its effectiveness critically depends on the quality of basic probability assignments (BPAs)…
Seewoo Li, Guemin Lee
The article proposes a new approach to estimating the latent distribution of item response theory (IRT) using kernel density estimation (KDE), particularly the solve-the-equation (STE) algorithm developed by Sheather and Jones (1991). As with existing methods, the KDE method aims to estimate the latent distribution of…
Nicholas Tenkorang, Kwesi Appau Ohene-Obeng, Xiaogang Su
Kernel Density Estimation (KDE) is a cornerstone of nonparametric statistics, yet it remains sensitive to bandwidth choice, boundary bias, and computational inefficiency. This study revisits KDE through a principled convolutional framework, providing an intuitive model-based derivation that naturally extends to…
Matti Karppa, Martin Aumüller, Rasmus Pagh
Kernel Density Estimation (KDE) is a nonparametric method for estimating the shape of a density function, given a set of samples from the distribution. Recently, locality-sensitive hashing, originally proposed as a tool for nearest neighbor search, has been shown to enable fast KDE data structures. However, these…
Maciej Skórski
Kernel Density Estimation is a very popular technique of approximating a density function from samples. The accuracy is generally well-understood and depends, roughly speaking, on the kernel decay and local smoothness of the true density. However concrete statements in the literature are often invoked in very specific…
Adriano Zanin Zambom, Ronaldo Dias
Nonparametric density estimation is of great importance when econometricians want to model the probabilistic or stochastic structure of a data set. This comprehensive review summarizes the most important theoretical aspects of kernel density estimation and provides an extensive description of classical and modern data…
Manuel Álvarez Chaves, Hoshin V. Gupta, Uwe Ehret, Anneli Guthke + 1 more
'Daniel Keren'] Using information-theoretic quantities in practical applications with continuous data is often hindered by the fact that probability density functions need to be estimated in higher dimensions, which can become unreliable or even computationally unfeasible. To make these useful quantities more…
David P. Hofmeyr
This paper presents new methodology for computationally efficient kernel density estimation. It is shown that a large class of kernels allows for exact evaluation of the density estimates using simple recursions. The same methodology can be used to compute density derivative estimates exactly. Given an ordered sample…
Ali Şenol
The cluster evaluation process is of great importance in areas of machine learning and data mining. Evaluating the clustering quality of clusters shows how much any proposed approach or algorithm is competent. Nevertheless, evaluating the quality of any cluster is still an issue. Although many cluster validity indices…
Arsalane Chouaib Guidoum
The kedd package [16] providing additional smoothing techniques to the R statistical system. Although various packages on the Comprehensive R Archive Network (CRAN) provide functions useful to nonparametric statistics, kedd aims to serve as a central location for more specifically of a nonparametric functions and data…
Johan Hallberg Szabadváry
The Beta kernel estimator offers a theoretically superior alternative to the Gaussian kernel for unit interval data, eliminating boundary bias without requiring reflection or transformation. However, its adoption remains limited by the lack of a reliable bandwidth selector; practitioners currently rely on iterative…
Zhengwu Zhang, Eric Klassen, Anuj Srivastava
While spherical data arises in many contexts, including in directional statistics, the current tools for density estimation and population comparison on spheres are quite limited. Popular approaches for comparing populations (on Euclidean domains) mostly involve a two-step procedure: (1) estimate probability density…
J. Emmanuel Johnson, Valero Laparra, Adrián Pérez-Suay, Miguel D. Mahecha + 2 more
Kernel methods are powerful machine learning techniques which use generic non-linear functions to solve complex tasks. They have a solid mathematical foundation and exhibit excellent performance in practice. However, kernel machines are still considered black-box models as the kernel feature mapping cannot be accessed…
Frank Kwasniok, Bryan C Daniels
A comprehensive methodology for semiparametric probability density estimation is introduced and explored. The probability density is modelled by sequences of mostly regular or steep exponential families generated by flexible sets of basis functions, possibly including boundary terms. Parameters are estimated by global…
Jayanth Vyasanakere, Jayanti Ray-Mukherjee, Rahul Joseph Fernandez
One of the central challenges in ecology and animal behaviour is to generate animal home range estimations. Kernel density estimate for home range has been one of the most widely used estimates these last few decades, despite its limitations. More recently a network-based kernel density (NKDE) approach has been…
Cong Ma, Carl Kingsford
Mutual information is widely used to characterize dependence between biological signals, such as co-expression between genes or co-evolution between amino acids. However, measurement error of the biological signals is rarely considered in estimating mutual information. Measurement error is widespread and non-negligible…
Gayatri Anand, Christen H. Fleming, Ananke G. Krishnan, Clayton T. Lamb + 4 more
Quantifying the space requirements of a population is a fundamental problem in spatial ecology, particularly as it relates to the identification of important utilization areas and the designation of protected areas for conservation and wildlife management. Traditionally, population space use estimation techniques scale…
Erin E. Fowler, Anders Berglund, Michael J. Schell, Thomas A. Sellers + 2 more
Limited sample sizes can hinder biomedical research and lead to spurious findings. The objective of this work is to present a new method to generate synthetic populations (SPs) from sparse data samples to aid in modeling developments. Matched case-control data (n=180 pairs) defined the limited samples. Cases and…
Jack Hollins, Christen Fleming, Justin M. Calabrese, Les Harris + 6 more
An animal’s home range plays a fundamental role in determining its resource use and overlap with conspecifics, competitors and predators, and is therefore a common focus of movement ecology studies. Autocorrelated kernel density estimation addresses many of the shortcomings of traditional home range estimators when…
Adam Stanski, Olaf Hellwich, Ioannis P. Androulakis
A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial…
Chaitanya Chintaluri, Marta Kowalska, Władysław Średniawa, Michał Czerwiński + 3 more
Kernel Current Source Density (kCSD), which we introduced in 2012, is a kernel-based method to estimate current source density (CSD) from extracellular potentials recorded with arbitrarily placed electrodes. Estimating reconstruction errors in CSD has been an outstanding challenge. To address it, here we revisit kCSD…
Stefano Mammola, Pedro Cardoso
The use of kernel density n-dimensional hypervolumes [Global Ecol. Biogeogr. 23(5):595–609] in trait-based ecology is rapidly increasing. By representing the functional space of a species or community as a Hutchinsonian niche space, this relatively new approach is showing great potential for the advance of functional…
Patchanok Srisuradetchai, Korn Suksrikran
The k-nearest neighbors (KNN) regression method, known for its nonparametric nature, is highly valued for its simplicity and its effectiveness in handling complex structured data, particularly in big data contexts. However, this method is susceptible to overfitting and fit discontinuity, which present significant…
Nickolas Gantzler, Aryan Deshwal, Janardhan Rao Doppa, Cory Simon
Our objective is to search a large candidate set of covalent organic frameworks (COFs) for the one with the largest equilibrium adsorptive selectivity for xenon (Xe) over krypton (Kr) at room temperature. To predict the Xe/Kr selectivity of a COF structure, we have access to two molecular simulation techniques: (1) a…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
Authors not listed
Plastic mechanical recycling is the conventional technological step towards circularity. In such aspects, complex mixtures of polyolefin blends are often fed into mechanical recycling systems, resulting in moulded products with uncertain quality. To add to the difficulty of heterogeneous feedstocks, the testing of…
Ping Yang, E. Adrian Henle, Xiaoli Fern, Cory M. Simon
Pesticides benefit agriculture by increasing crop yield, quality, and security. However, pesticides may inadvertently harm bees, which are agriculturally and ecologically vital as pollinators. The development of new pesticides---driven by pest resistance to and demands to reduce negative environmental impacts of…