Maximum likelihood estimation of nonnegative trigonometric sum models using a Newton-like algorithm on manifolds

DOI10.1214/10-EJS587zbMath1264.49029MaRDI QIDQ1952108

Juan José Fernández-Durán, María Mercedes Gregorio-Domínguez

Publication date: 27 May 2013

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ejs/1291903543

zbMATH Keywords

maximum likelihood estimationdifferential geometryNewton algorithmsmooth Riemann manifoldnonnegative Fourier series

Mathematics Subject Classification ID

Point estimation (62F10) Newton-type methods (49M15) Numerical computation of solutions to systems of equations (65H10)

Related Items (8)

Modeling angles in proteins and circular genomes using multivariate angular distributions based on multiple nonnegative trigonometric sums ⋮ Seasonal mortality for fractional ages in short term life insurance ⋮ Test of bivariate independence based on angular probability integral transform with emphasis on circular-circular and circular-linear data ⋮ Distributions for spherical data based on nonnegative trigonometric sums ⋮ Bayesian nonparametrics for directional statistics ⋮ Exact risk improvement of bandwidth selectors for kernel density estimation with directional data ⋮ Consumer segmentation based on use patterns ⋮ Bayesian analysis of circular distributions based on non-negative trigonometric sums

Uses Software

• R
• CircNNTSR

Cites Work

• Some global optimization problems on Stiefel manifolds
• Statistical Analysis of Circular Data
• Circular Distributions Based on Nonnegative Trigonometric Sums
• Optimization algorithms exploiting unitary constraints
• Models for Circular-Linear and Circular-Circular Data Constructed from Circular Distributions Based on Nonnegative Trigonometric Sums
• Multi-sample tests for the Fisher distribution for directions
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