Much recent work in CA employs diffeomorphisms that deform templates into observed images. See Christensen, Rabbit, Miller (1994), Beg, Miller,Trouve, and Younes (2003), Mumford (2002), Miller, Trouve and Younes (2002). Further reference can be found at www.cis.jhu.edu and in Grenander,Miller (1998). This work has used a static approach, biological time does not enter the discussion, only algor...

state estimation is the foundation of any control and decision making in power networks. the first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. this paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator...

Salah satu hal penting dalam analisis statistik adalah prosedur estimasi suatu fungsi padat peluang yang biasa disebut densitas. Ada dua metode pendekatan biasanya digunakan, yaitu parameter terkait dengan asumsi distribusi tertentu dan densitas secara non parametrik. Metode parametrik sering kita jumpai histogram.
 Beberapa kelemahan histogram menjadi acuan untuk dikembangkannya lain kern...

the gastric cancer in iran is the fourth in the general population. this study was designed to determine the five-year survival rate of gastric cancer patients, and to assess its associated factors. we analyzed the data using a time-dependent covariates model, and recommend it for analyses of similar data. 281 gastric cancer patients with adenocarcinomatous pathology who had been operated on at...

kernel density estimators are the basic tools for density estimation in non-parametric statistics.  the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in  which  the  bandwidth  is varied depending on the location of the sample points. in this paper‎, we  initially introduce the k-nearest neighbor kernel density estimator in the random left-truncatio...

in statistical inference, the point estimation problem is very crucial and has a wide range of applications. when, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. therefore, the theory of fuzzy sets plays an important role in formulating such situations. in this paper, we rst recall the crisp uniformly minimum ...

in this paper, drift change point estimation in the mean of polynomial profiles is considered. for this purpose, the proposed change point estimator is computed using maximum likelihood approach. performance of the proposed estimator is evaluated using monte carlo simulations when t2 control chart issues an out-of-control signal. simulation results show that the performance of the proposed esti...