• Title/Summary/Keyword: Generalized Inverse

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Spatial Resolution Improvement of landsat TM Images Using a SPOT PAN Image Data Based on the New Generalized Inverse Matrix Method (새로운 일반화 역행렬법에 의한 SPOT PAN 화상 데이터를 이용한 Landsat TM 화상이 공간해상도 개선)

  • 서용수;이건일
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.31B no.8
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    • pp.147-159
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    • 1994
  • The performance of the improvement method of spatial resolution for satellite images based on the generalized inverse matrix is superior to the conventional methods. But, this method calculates the coefficient values for extracting the spatial information from the relation between a small pixel and large pixels. Accordingly it has the problem of remaining the blocky patterns at the result image. In this paper, a new generalized inverse matrix method is proposed which is different in the calculation method of coefficient values for extracting the spatial information. In this proposed metod, it calculates the coefficient values for extracting the spatial information from the relation between a small pixel and small pixels. Consequently it can improve the spatial resolution more efficiently without remaining the blocky patterns at the result image. The effectiveness of the proposed method is varified by simulation experiments with real TM image data.

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Bayesian and maximum likelihood estimation of entropy of the inverse Weibull distribution under generalized type I progressive hybrid censoring

  • Lee, Kyeongjun
    • Communications for Statistical Applications and Methods
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    • v.27 no.4
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    • pp.469-486
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    • 2020
  • Entropy is an important term in statistical mechanics that was originally defined in the second law of thermodynamics. In this paper, we consider the maximum likelihood estimation (MLE), maximum product spacings estimation (MPSE) and Bayesian estimation of the entropy of an inverse Weibull distribution (InW) under a generalized type I progressive hybrid censoring scheme (GePH). The MLE and MPSE of the entropy cannot be obtained in closed form; therefore, we propose using the Newton-Raphson algorithm to solve it. Further, the Bayesian estimators for the entropy of InW based on squared error loss function (SqL), precautionary loss function (PrL), general entropy loss function (GeL) and linex loss function (LiL) are derived. In addition, we derive the Lindley's approximate method (LiA) of the Bayesian estimates. Monte Carlo simulations are conducted to compare the results among MLE, MPSE, and Bayesian estimators. A real data set based on the GePH is also analyzed for illustrative purposes.

MOORE-PENROSE INVERSE IN AN INDEFINITE INNER PRODUCT SPACE

  • KAMARAJ K.;SIVAKUMAR K. C.
    • Journal of applied mathematics & informatics
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    • v.19 no.1_2
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    • pp.297-310
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    • 2005
  • The concept of the Moore-Penrose inverse in an indefinite inner product space is introduced. Extensions of some of the formulae in the Euclidean space to an indefinite inner product space are studied. In particular range-hermitianness is completely characterized. Equivalence of a weighted generalized inverse and the Moore-Penrose inverse is proved. Finally, methods of computing the Moore-Penrose inverse are presented.

COMPUTING GENERALIZED INVERSES OF A RATIONAL MATRIX AND APPLICATIONS

  • Stanimirovic, Predrag S.;Karampetakis, N. P.;Tasic, Milan B.
    • Journal of applied mathematics & informatics
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    • v.24 no.1_2
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    • pp.81-94
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    • 2007
  • In this paper we investigate symbolic implementation of two modifications of the Leverrier-Faddeev algorithm, which are applicable in computation of the Moore-Penrose and the Drazin inverse of rational matrices. We introduce an algorithm for computation of the Drazin inverse of rational matrices. This algorithm represents an extension of the papers [11] and [14]. and a continuation of the papers [15, 16]. The symbolic implementation of these algorithms in the package MATHEMATICA is developed. A few matrix equations are solved by means of the Drazin inverse and the Moore-Penrose inverse of rational matrices.

Estimating causal effect of multi-valued treatment from observational survival data

  • Kim, Bongseong;Kim, Ji-Hyun
    • Communications for Statistical Applications and Methods
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    • v.27 no.6
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    • pp.675-688
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    • 2020
  • In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

INVERTIBILITY OF GENERALIZED BESSEL MULTIPLIERS IN HILBERT C-MODULES

  • Tabadkan, Gholamreza Abbaspour;Hosseinnezhad, Hessam
    • Bulletin of the Korean Mathematical Society
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    • v.58 no.2
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    • pp.461-479
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    • 2021
  • This paper includes a general version of Bessel multipliers in Hilbert C∗-modules. In fact, by combining analysis, an operator on the standard Hilbert C∗-module and synthesis, we reach so-called generalized Bessel multipliers. Because of their importance for applications, we are interested to determine cases when generalized multipliers are invertible. We investigate some necessary or sufficient conditions for the invertibility of such operators and also we look at which perturbation of parameters preserve the invertibility of them. Subsequently, our attention is on how to express the inverse of an invertible generalized frame multiplier as a multiplier. In fact, we show that for all frames, the inverse of any invertible frame multiplier with an invertible symbol can always be represented as a multiplier with an invertible symbol and appropriate dual frames of the given ones.

Development of Inverse Solver based on TSVD in Electrical Impedance Tomography (전기 임피던스 단층촬영법에서 TSVD 기반의 역문제 해법의 개발)

  • Kim, Bong Seok;Kim, Chang Il;Kim, Kyung Youn
    • Journal of the Institute of Electronics and Information Engineers
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    • v.54 no.4
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    • pp.91-98
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    • 2017
  • Electrical impedance tomography is a nondestructive imaging technique to reconstruct unknown conductivity distribution based on applied current data and measured voltage data through an array of electrodes attached on the periphery of a domain. In this paper, an inverse method based on truncated singular value decomposition is proposed to solve the inverse problem with the generalized Tikhonov regularization and to reconstruct the conductivity distribution. In order to reduce the inverse computational time, truncated singular value decomposition is applied to the inverse term after the generalized regularization matrix is taken out from the inverse matrix term. Numerical experiments and phantom experiments have been performed to verify the performance of the proposed method.

EXPLICIT MINIMUM POLYNOMIAL, EIGENVECTOR AND INVERSE FORMULA OF DOUBLY LESLIE MATRIX

  • WANICHARPICHAT, WIWAT
    • Journal of applied mathematics & informatics
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    • v.33 no.3_4
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    • pp.247-260
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    • 2015
  • The special form of Schur complement is extended to have a Schur's formula to obtains the explicit formula of determinant, inverse, and eigenvector formula of the doubly Leslie matrix which is the generalized forms of the Leslie matrix. It is also a generalized form of the doubly companion matrix, and the companion matrix, respectively. The doubly Leslie matrix is a nonderogatory matrix.

SEMILOCAL CONVERGENCE THEOREMS FOR A CERTAIN CLASS OF ITERATIVE PROCEDURES

  • Ioannis K. Argyros
    • Journal of applied mathematics & informatics
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    • v.7 no.1
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    • pp.29-40
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    • 2000
  • We provide semilocal convergence theorems for Newton-like methods in Banach space using outer and generalized inverses. In contrast to earlier results we use hypotheses on the second instead of the first Frechet-derivative. This way our Newton-Kantorovich hypotheses differ from earlier ones. Our results can be used to solve undetermined systems, nonlinear least square problems and ill-posed nonlinear operator equations.