Minimization technique for a convex function with application to multiple regression model
DOI10.1080/02331938808843342zbMath0648.65100OpenAlexW2000920236MaRDI QIDQ3793681
K. Anthony Rhee, WanSoo T. Rhee
Publication date: 1988
Published in: Optimization (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/02331938808843342
parameter estimationconvex functionfittingmaximum likelihood estimatorsimplex methodleast squares methodmultiple linear regression model
Numerical smoothing, curve fitting (65D10) Linear regression; mixed models (62J05) Numerical mathematical programming methods (65K05) Linear programming (90C05) Probabilistic methods, stochastic differential equations (65C99)
Cites Work
- Solutions of overdetermined linear equations which minimize error in an abstract norm
- An interval programming algorithm for discrete linear \(L_ 1\) approximation problems
- A revised simplex algorithm for the absolute deviation curve fitting problem
- A new descent algorithm for the least absolute value regression problem
- An Efficient Algorithm for Discrete $l_1$ Linear Approximation with Linear Constraints
- An Algorithm for $l_1 $-Norm Minimization with Application to Nonlinear $l_1 $-Approximation
- An Iterative Technique for Absolute Deviations Curve Fitting
- A Rapidly Convergent Descent Method for Minimization
- Function minimization by conjugate gradients
- On $L_1 $ Approximation II: Computation for Discrete Functions and Discretization Effects
- Chebyshev and $l^1 $-Solutions of Linear Equations Using Least Squares Solutions
- $L_p $-Criteria for the Estimation of Location Parameters
- Two Linear Programming Algorithms for Unbiased Estimation of Linear Models
- An Improved Algorithm for Discrete $l_1 $ Linear Approximation
- Norms for Smoothing and Estimation
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