A new method for approximating the likelihood 39
A new method for approximating vector autoregressive processes by finite state Markov chains
... the method works in terms of approximating autoregressive processes for various degrees of persistence of the discrete ...the new method outperforms the method by Tauchen (1986a), ...
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A new method based on the manifold alternative approximating for low rank matrix completion
... a new method is proposed for low-rank matrix completion which is based on the least squares approximating to the known elements in the manifold formed by the singular vectors of the partial singular ...
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GAPPARD: a computationally efficient method of approximating gap-scale disturbance in vegetation models
... latter method increases the number of calculations by two to three orders of magnitude compared to the less realistic population average ...a new method for simulating stand-replacing disturbances ...
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Approximating Likelihood Ratios with Calibrated Discriminative Classifiers
... generalized likelihood ratio tests are established tools for statistical ...the likelihood function for a given observation x, which motivates a new class of likelihood-free inference ...that ...
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A New Method for Computing Elimination Ideals of Likelihood Equations
... Maximum likelihood estimation, Likelihood equation, Real root classification, Discriminant, Elimination ideal ACM Reference Format: Xiaoxian Tang, Timo de Wolff, and Rukai ...A New Method for ...
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A New Jackknife Empirical Likelihood Method for U-Statistics
... empirical likelihood (EL) for U-statistics is computationally ex- pensive because of its nonlinear ...empirical likelihood method largely relieves computation burden by circumventing the construction ...
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Approximating Hamiltonian dynamics with the Nyström method
... Simulating the time-evolution of quantum mechanical systems is BQP-hard and expected to be one of the foremost applications of quantum computers. We consider classical algorithms for the approximation of Hamiltonian ...
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Empirical likelihood method
... l likelihood given in C h a p t e r 1 and the work discussed in C h a p t e r 2, 3 and 4, we see t h a t a l mo s t all th e research done on empirical likelihood c o n ce n t r at e on c o n s t r u c t i ...
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A COMPUTER METHOD FOR APPROXIMATING THE ZEROS OF CERTAIN ENTIRE FUNCTIONS
... AMY C. The computer revolution has greatly enhanced techni ques used in numerical calculations for a wide range of functions. This paper investigates some properties of a certain class[r] ...
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Usage of Penalized Maximum Likelihood Estimation Method in Medical Research: An Alternative to Maximum Likelihood Estimation Method
... using new approach (Penalized Maximum Likelihood Estimation (PMLE) Method) in Logistic ...PMLE method were found ...PMLE method for separation problem. According to PMLE Method, ...
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Deep learning methods for likelihood-free inference :approximating the posterior distribution with convolutional neural networks
... minimum predicted non-decision time), thus rendering the total likelihood to be zero as well. Therefore, the parameter search is forced to adopt values that can encompass all data, that is, also the one fast ...
128
An optimal method for approximating the delay differential equations of noninteger order
... a method with a free parameter, named the optimal asymptotic homotopy method (OHAM), in order to obtain the solution of delay differential equations, delay partial differential equations, and a system of ...
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Reduce computation in profile empirical likelihood method
... Given a sample X of increments X with sample size n, we are interested in the hypothesis H 0 : µ = 0. This amounts to asking whether it is suffi- cient to model the data in interest using a martingale process. For ...
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Marginal Likelihood Estimation with the Cross Entropy Method
... 5 Empirical Applications In this section, we present two empirical examples to illustrate the proposed importance sam- pling approach for estimating the marginal likelihood. In each of these examples, the ...
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Application of a weighted likelihood method to hypocenter determination
... The method of least squares is a standard approach to hypocenter determination in ...this method is not useful for data contaminated by systematic ...weighted likelihood method (WLL) rather ...
6
Reduce computation in profile empirical likelihood method
... Given a sample X of increments X with sample size n, we are interested in the hypothesis H 0 : µ = 0. This amounts to asking whether it is suffi- cient to model the data in interest using a martingale process. For ...
39
A Maximum Likelihood Method for the Incidental Parameter Problem
... the likelihood of the maximal invariant statistic yields the maximum invariant likelihood estimator ...our method to (i) a stationary autoregressive model with fixed effects; (ii) an agent-specific ...
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A projection method for approximating fixed points of quasinonexpansive mappings in Hadamard spaces
... This work is devoted to analyzing the feasibility study of a Moudafi viscosity projection method with a weak contraction for a finite family of quasinonexpansive mappings in a Hadamard space. To this end, we need to ...
13
Approximating turbulent and non-turbulent events with the Tensor Train decomposition method
... In this study, we apply the Tensor Train decomposition method to flow profiles of computational and experimental fluid dynamics. We found the Tensor Train format to be an efficient method to compress big ...
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On the Nyström Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
... Nystr¨om method from integral equation theory; this method has also been applied recently in the learning theory community to approximate the solution of spectral partitioning for image and video ...
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