Results 11 to 20 of about 782 (91)
Estimation and inference in adaptive learning models with slowly decreasing gains
Journal of Time Series Analysis, Volume 43, Issue 5, Page 720-749, September 2022., 2022 An asymptotic theory for estimation and inference in adaptive learning models with strong mixing regressors and martingale difference innovations is developed. The maintained polynomial gain specification provides a unified framework which permits slow convergence of agents' beliefs and contains recursive least squares as a prominent special case ...
Alexander Mayer
wiley +1 more source
Properties and Inference for a New Class of Generalized Rayleigh Distributions with an Application
Open Mathematics, 2019 In the present paper, we introduce a new form of generalized Rayleigh distribution called the Alpha Power generalized Rayleigh (APGR) distribution by following the idea of extension of the distribution families with the Alpha Power transformation.
Biçer Hayrinisa Demirci
doaj +1 more source
Power Birnbaum-Saunders Student t distribution
Revista Integración, 2017 The fatigue life distribution proposed by Birnbaum and Saunders has been used quite effectively to model times to failure for materials subject to fatigue.
Germán Moreno-Arenas +2 more
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On decompositions of estimators under a general linear model with partial parameter restrictions
Open Mathematics, 2017 A general linear model can be given in certain multiple partitioned forms, and there exist submodels associated with the given full model. In this situation, we can make statistical inferences from the full model and submodels, respectively.
Jiang Bo, Tian Yongge, Zhang Xuan
doaj +1 more source
Locally adaptive estimation methods with application to univariate time series [PDF]
, 2008 The paper offers a unified approach to the study of three locally adaptive estimation methods in the context of univariate time series from both theoretical and empirical points of view. A general procedure for the computation of critical values is given.
Elagin, Mstislav
core +3 more sources
Maximum likelihood estimator of the volatility of forward rates driven by geometric spatial AR sheet
Journal of Applied Mathematics, Volume 2004, Issue 4, Page 293-309, 2004., 2004 Discrete‐time forward interest rate curve models are studied, where the curves are driven by a random field. Under the assumption of no‐arbitrage, the maximum likelihood estimator of the volatility parameter is given and its asymptotic behaviour is studied. First, the so‐called martingale models are examined, but we will also deal with the general case,
József Gáll +2 more
wiley +1 more source
A note on Hammersley′s inequality for estimating the normal integer mean
International Journal of Mathematics and Mathematical Sciences, Volume 2003, Issue 34, Page 2147-2156, 2003., 2003 Let X1, X2, …, Xn be a random sample from a normal N(θ, σ2) distribution with an unknown mean θ = 0, ±1, ±2, …. Hammersley (1950) proposed the maximum likelihood estimator (MLE) d=[X¯n], nearest integer to the sample mean, as an unbiased estimator of θ and extended the Cramér‐Rao inequality.
Rasul A. Khan
wiley +1 more source
Estimating the COGARCH(1,1) model - a first go [PDF]
, 2005 We suggest moment estimators for the parameters of a continuous time GARCH(1,1) process based on equally spaced observations. Using the fact that the increments of the COGARCH(1,1) process are ergodic, the resulting estimators are consistent.
Haug, Stephan +3 more
core +2 more sources
On hypergeometric generalized negative binomial distribution
International Journal of Mathematics and Mathematical Sciences, Volume 29, Issue 12, Page 727-736, 2002., 2002 It is shown that the hypergeometric generalized negative binomial distribution has moments of all positive orders, is overdispersed, skewed to the right, and leptokurtic. Also, a three‐term recurrence relation for computing probabilities from the considered distribution is given.
M. E. Ghitany +2 more
wiley +1 more source
An iterative tomogravity algorithm for the estimation of network traffic
, 2007 This paper introduces an iterative tomogravity algorithm for the estimation of a network traffic matrix based on one snapshot observation of the link loads in the network.
Fang, Jiangang +2 more
core +1 more source