scholarly journals ESTIMASI KURVA REGRESI SPLINE PADA DATA LONGITUDINAL DENGAN METODE KUADRAT TERKECIL

Intersections ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 17-25
Author(s):  
Toto Hermawan
Keyword(s):  
Longitudinal Data ◽  
Least Squares ◽  
Linear Estimation ◽  
Spline Regression ◽  
Regression Curve ◽  
Time Periods

This article examines the estimation of spline regression, especially its use in longitudinal data. Longitudinal data is data obtained based on observations made as many as n objects that are independent with each object being repeatedly observed in different time periods and between observations in the same object are dependent besides longitudinal data is data that can distinguish the diversity of responses caused by The regression curve of the spline was estimated using the least squares. It can be seen that the estimation of the spline regression curve for longitudinal data is a class of linear estimation in response observations and is highly dependent on the knots point k1,k2,...,Kn

scholarly journals Estimation and filtering of fractional generalised random fields

2000 ◽  
Vol 69 (3) ◽  
pp. 336-361 ◽  
Author(s):  
J. M. Angulo ◽  
M. D. Ruiz-Medina ◽  
V. V. Anh
Keyword(s):  
Brownian Motion ◽  
Random Field ◽  
Least Squares ◽  
Random Fields ◽  
General Class ◽  
Duality Theory ◽  
Weak Sense ◽  
Linear Estimation ◽  
Special Cases ◽  
Estimation And Filtering

AbstractThis paper considers the estimation and filtering of fractional random fields, of which fractional Brownian motion and fractional Riesz-Bessel motion are important special cases. A least-squares solution to the problem is derived by using the duality theory and covariance factorisation of fractional generalised random fields. The minimum fractional duality order of the information random field leads to the most general class of solutions corresponding to the largest function space where the output random field can be approximated. The second-order properties that define the class of random fields for which the least-squares linear estimation problem is solved in a weak-sense are also investigated in terms of the covariance spectrum of the information random field.

Bayes least squares linear estimation of densities

1984 ◽  
Vol 13 (18) ◽  
pp. 2253-2291 ◽  
Author(s):  
John Shin ◽  
H. D Brunk
Keyword(s):  
Least Squares ◽  
Linear Estimation

scholarly journals Least-squares linear estimation of signals from observations with Markovian delays

2011 ◽  
Vol 236 (2) ◽  
pp. 234-242 ◽  
Author(s):  
M.J. García-Ligero ◽  
A. Hermoso-Carazo ◽  
J. Linares-Pérez
Keyword(s):  
Least Squares ◽  
Linear Estimation

More powerful cointegration tests with non-normal errors

2015 ◽  
Vol 19 (4) ◽  
Author(s):  
Hyejin Lee ◽  
Junsoo Lee ◽  
Kyungso Im
Keyword(s):  
Least Squares ◽  
Testing Procedure ◽  
Error Distribution ◽  
Linear Estimation ◽  
Moment Conditions ◽  
Significant Power ◽  
Cointegration Tests ◽  
Normal Error ◽  
Simulation Results ◽  
Normal Error Distribution

AbstractIn this paper, we suggest new cointegration tests that can become more powerful in the presence of non-normal errors. Non-normal errors will not pose a problem in usual cointegration tests even when they are ignored. However, we show that they can become useful sources to improve the power of the tests when we use the “residual augmented least squares” (RALS) procedure to make use of nonlinear moment conditions driven by non-normal errors. The suggested testing procedure is easy to implement and it does not require any non-linear estimation techniques. We can exploit the information on the non-normal error distribution that is already available but ignored in the usual cointegration tests. Our simulation results show significant power gains over existing cointegration tests in the presence of non-normal errors.

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