Dependent Variable: Y Method: Least Squares Date: 05/23/00 Time: 05:55 Sample: 1 33 Included observations: 33 Variable Coefficient Std. Error t-Statistic Prob. C 102192.4 12799.83 7.983891 0.0000 N -9074.674 2052.674 -4.420904 0.0001 P 0.354668 0.072681 4.879810 0.0000 I 1.287923 0.543294 2.370584 0.0246 R-squared 0.618154 Mean dependent var 125634.6 Adjusted R-squared 0.578653 S.D. dependent v...

We show that some widely accepted criteria for cryptographic functions, including the strict avalanche criterion (SAC) and the propagation criterion, have various limitations in capturing properties of vital importance to cryptographic algorithms, and propose a new criterion called GAC to measure the global avalanche characteristics of cryptographic functions. We also introduce two indicators r...

This paper reviews the development of a root mean squared error (RMS) flaw sizing criterion. The effect of using an RMS error criterion for the sizing performance of the PISC II [1-3] inspection teams is investigated. A crucial part of establishing an RMS error sizing criterion is the decomposition of the components in the RMS error into the sum of the squares of the mean error, the standard er...

We present a methodology for adaptive ltering and system identi cation under the cyclostationary regime. Our technique is based on a deterministic periodic least-squares criterion, and gives rise to adaptive periodic recursive-least-squares (P-RLS) algorithms. Furthermore, we show that every adaptive RLS algorithm has a P-RLS counterpart, which has exactly the same architecture and the same per...

In 1929, Mahler proved that the real number generated by Thue–Morse sequence is transcendental. Later, Adamczewski and Bugeaud gave a different proof of transcendence this using combinatorial criterion. Moreover, Kumar Meher generalization criterion under subspace Lang conjecture. paper, we prove conjecture along squares transcendental Meher.

Namyong Kim 155 In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization ...

Choosing the performance criterion to be mean squared error matrix, we have compared the least squares and Stein-rule estimators for coefficients in a linear regression model when the disturbances are not necessarily normally distributed. It is shown that none of the two estimators dominates the other, except in the trivial case of merely one regression coefficient where least squares is found ...

The problem of assessing the performance of algorithms used for the minimization of an l1-penalized least-squares functional, for a range of penalization parameters, is investigated. A criterion that uses the idea of ‘approximation isochrones’ is introduced.