Search results for "Location parameter"
showing 6 items of 6 documents
A new weighted normal-based filter for 3D mesh denoising
2018
In this paper, we propose a normal based filtering method for 3D mesh denoising. For this purpose, we compute the new triangle normal vectors by using a weighted sum of the average (smoothness) and the myriad (sharpness) filters in each neighborhood. These weights, that reflect the degree of the surface sharpness, are calculated according to the statistical distribution of the angles between the normal vectors of the triangles. The histogram of the angles between surface normal vectors is accurately fitted by the well known Cauchy distribution. Here, we justify the use of the myriad filter whose estimated value represents the optimum of the location parameter of the investigated distributio…
Maximum probability estimators in the case of exponential distribution
1975
In 1966–1969L. Weiss andJ. Wolfowitz developed the theory of „maximum probability” estimators (m.p.e.'s). M.p.e.'s have the property of minimizing the limiting value of the risk (see (2.10).) In the present paper, therfore, after a short description of the new method, a fundamental loss function is introduced, for which—in the so-called regular case—the optimality property of the maximum probability estimators yields the classical result ofR.A. Fisher on the asymptotic efficiency of the maximum likelihood estimator. Thereby it turns out that the m.p.e.'s possess still another important optimality property for this loss function. For the latter the parameters of the exponential distribution—…
Posterior moments and quantiles for the normal location model with Laplace prior
2021
We derive explicit expressions for arbitrary moments and quantiles of the posterior distribution of the location parameter η in the normal location model with Laplace prior, and use the results to approximate the posterior distribution of sums of independent copies of η.
Sequential estimation of a location parameter and powers of a scale parameter from delayed observations
2013
The problem of sequentially estimating a location parameter and powers of a scale parameter is considered in the case when the observations become available at random times. Certain classes of sequential estimation procedures are derived under an invariant balanced loss function and with the observation cost determined by a convex function of the stopping time and the number of observations up to that time.
Some Remarks on Exponential Families
1987
Abstract The following facts may serve to provide a feeling about how restrictive the assumption of an exponential family is. (a) A one-parameter exponential family in standard form with respect to Lebesgue measure is a location parameter family iff it is normal with fixed variance. (b) It is a scale parameter family iff it is gamma with fixed shape parameter. Both facts are known (see Borges and Pfanzagl 1965; Ferguson 1962; Lindley 1958) but may not have received as much attention as they deserve. Under the assumption of differentiable densities, short and elementary proofs are given.
Adaptīvās nošķeltās vidējās vērtības metode
2022
Darbā tiek izpētītas zinātniskajā literatūrā līdz šim zināmas adaptīvās nošķeltās vidējās vērtības metodes, kā arī vispārīgi aprakstītas robustās statistikas metodes.