Results 91 to 100 of about 3,471 (207)
Extremely Fast Maximum Likelihood Estimation of High‐Order Autoregressive Models
Journal of Time Series Analysis, Volume 47, Issue 4, Page 876-884, July 2026.ABSTRACT We consider the problem of exact maximum likelihood estimation of potentially high‐order (p>50) autoregressive models. We propose an extremely fast coordinate‐wise algorithm for fitting autoregressive models. This fast algorithm exploits several properties of the negative log‐likelihood when parameterised in terms of partial autocorrelations ...
Daniel F. Schmidt, Enes Makalic
wiley +1 more source
Regression Asymptotics Using Martingale Convergence Methods [PDF]
Weak convergence of partial sums and multilinear forms in independent random variables and linear processes to stochastic integrals now plays a major role in nonstationary time series and has been central to the development of unit root econometrics. The
Peter C.B. Phillips, Rustam Ibragimov
core
Sustainable Optimizing Performance and Energy Efficiency in Proof of Work Blockchain: A Multilinear Regression Approach [PDF]
The energy-intensive characteristics of the computations performed by graphics processing units (GPUs) in proof-of-work (PoW) blockchain technology are readily apparent.
Songwut Boonsong +2 more
core +1 more source
Energies, 2019 In order to reduce the calculation effort during the simulation of the emission of polychlorinated dibenzo-p-dioxins and furans (PCDD/F) during municipal solid waste incineration, minimizing the number of simulated components is mandatory.
Damià Palmer +5 more
doaj +1 more source
Nigerian Journal of Technology, 2018 The application of Multi-Linear Regression Analysis (MLRA) model for predicting soil properties in Calabar South offers a technical guide and solution in foundation designs problems in the area. Forty-five soil samples were collected from fifteen different boreholes at a different depth and 270 tests were carried out for CBR, MC, SG, LL, PL test and GS
Egbe, J.G. +4 more
openaire +3 more sources
Optimal Scaling of Interaction Effects in Generalized Linear Models [PDF]
Multiplicative interaction models, such as Goodman's RC(M) association models, can be a useful tool for analyzing the content of interaction effects. However, most models for interaction effects are only suitable for data sets with two or three predictor
Koning, A.J. +2 more
core +1 more source
Comparison of ANN, Regression Analysis, and ANFIS Models in Estimation of Global Solar Radiation for Different Climatological Locations [PDF]
, 2018 In this study, the monthly mean daily global solar radiation (GSR) is modeled by artificial neural network (ANN), multilinear regression analysis (MLRA), and adaptive network-based fuzzy inference system (ANFIS) methods in the eight cities of Turkey. The
Deniz, E. +3 more
core +1 more source
Water Quality Index Estimation Model for Aquaculture System Using Artificial Neural Network [PDF]
Journal of Advances in Computer Engineering and Technology, 2019 Water Quality plays an important role in attaining a sustainable aquaculture system, its cumulative effect can make or mar the entire system. The amount of dissolved oxygen (DO) alongside other parameters such as temperature, pH, alkalinity and ...
Taliha Folorunso +4 more
doaj
EEG data classification using multilinear regression model
, 2020 Beyin Bilgisayar Arayüzleri (BCI), kişilerin, sinir sistemlerini kullanmaksızın,elektromekanik veya nöroprostetik bir cihazı bilgisayar yardımıyla denetlemelerini sağlayan sistemlerdir. Kafaderisi üzerine yerleştirilen elektrotlardan elde edilen ve Elektroansefalogram (EEG) adı verilen elektriksel kayıtlar sayesinde beyin dokuları hakkındabilgi ...
openaire +1 more source
Tensor on tensor regression with tensor normal errors and tensor network states on the regression parameter [PDF]
, 2018 With the growing interest in tensor regression models and decompositions, the tensor normal distribution offers a flexible and intuitive way to model multi-way data and error dependence.
Llosa, Carlos
core +2 more sources