The matching of minority costume images is not only the field of image registration but also difference from the general image registration. In the image of minority costumes, there are cases where the color is rich a...
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The matching of minority costume images is not only the field of image registration but also difference from the general image registration. In the image of minority costumes, there are cases where the color is rich and the redundancy is more complicated, and there are a lot of bright colors and many of the same features. Based on these characteristics of minority costume images, we propose K-Means cluster preprocessing for images in the registration of minority costumes, so as to avoid the influence of some redundant points or noises in subsequent registration and thus improve the accuracy of registration. Then we use the classic registration algorithm to register the minority costume images and finally achieve the registration of minority costume images from two different angles.
In the recent work of Rodrigues et al. (2009), a flexible cure rate survival model was developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution. T...
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In the recent work of Rodrigues et al. (2009), a flexible cure rate survival model was developed by assuming the number of competing causes of the event of interest to follow the Conway-Maxwell Poisson distribution. This model includes as special cases some of the well-known cure rate models discussed in the literature. As the data obtained from cancer clinical trials are often subject to right censoring, the expectation maximization (em) algorithm can be used as a powerful and efficient tool for the estimation of the model parameters based on right censored data. In this paper, the cure rate model developed by Rodrigues et al. (2009) is considered and assuming the time-to-event to follow the exponential distribution, exact likelihood inference is developed based on the em algorithm. The inverse of the observed information matrix is used to compute the standard errors of the maximum likelihood estimates (MLEs). An extensive Monte Carlo simulation study is performed to illustrate themethod of inference developed here. Finally, the proposed methodology is illustrated with real data on cutaneous melanoma.
In 1997, Marshall and Olkin introduced a very powerful method to introduce an additional parameter to a class of continuous distribution functions that brings more flexibility to the model. They demonstrated their met...
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In 1997, Marshall and Olkin introduced a very powerful method to introduce an additional parameter to a class of continuous distribution functions that brings more flexibility to the model. They demonstrated their method for the exponential and Weibull classes. In the same paper they briefly indicated its bivariate extension. The main aim of this article is to introduce the same method, for the first time, to the class of discrete generalized exponential distributions both for the univariate and bivariate cases. We investigate several properties of the proposed univariate and bivariate classes. The univariate class has three parameters, whereas the bivariate class has five parameters. It is observed that depending on the parameter values, the univariate class can be zero inflated as well as heavy tailed. We propose to use an expectation-maximization (em) algorithm to estimate the unknown parameters. Small simulation experiments have been performed to see the effectiveness of the proposed em algorithm, and a bivariate data set has been analyzed;it is observed that the proposed models and the em algorithm work quite well in practice.
作者：
Lee, Jiyon114-302
Daecheong Ro 116Beon Gil 30 Hanam Si 12954 Gyeonggi Do South Korea
This paper applies the latent class approach to underlying spatial dynamic models. Spatial weights are assumed to be time-varying, although the class membership of each unit is fixed over time. The class-specific para...
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This paper applies the latent class approach to underlying spatial dynamic models. Spatial weights are assumed to be time-varying, although the class membership of each unit is fixed over time. The class-specific parameters and class memberships are estimated with em algorithm. The performance of the proposed model with a finite sample is examined using Monte Carlo simulation. (C) 2017 Elsevier B.V. All rights reserved.
Longitudinal data analysis has found immense importance in biomedical fields to assess relationships between an outcome and its explanatory variables over time. However, this analysis is unreliable in presence of meas...
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This paper presents a robust iterative identification scheme for a class of Hammerstein nonlinear ARMAX systems. The identification problem is formulated under the framework of maximum likelihood estimation and solved...
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ISBN:
(纸本)9789881563958
This paper presents a robust iterative identification scheme for a class of Hammerstein nonlinear ARMAX systems. The identification problem is formulated under the framework of maximum likelihood estimation and solved by the expectation-maximization (em) algorithm. Instead of modeling the ambient noise with a Gaussian distribution, the heavy tailed Gaussian mixture distribution is utilized, which ensures the estimation algorithm is robust to the outliers. By means of the over-parameterization method and replacing the unmeasurable noise terms with the estimation residuals, the iterative algorithm is able to identify the system model and the parameters of noise distribution simultaneously. The simulation results indicate the effectiveness of the proposed algorithm.
Among recent methods designed for accelerating the em algorithm without any modification in the structure of em or in the statistical model, the parabolic acceleration (P-em) has proved its efficiency. It does not inv...
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Among recent methods designed for accelerating the em algorithm without any modification in the structure of em or in the statistical model, the parabolic acceleration (P-em) has proved its efficiency. It does not involve any computation of gradient or hessian matrix and can be used as an additional software component of any fixed point algorithm maximizing some objective function. The vector epsilon algorithm was introduced to reach the same goals. Through geometric considerations, the relationships between the outputs of an improved version of P-em and those of the vector epsilon algorithm are established. This sheds some light on their different behaviours and explains why the parabolic acceleration of em outperforms its competitor in most numerical experiments. A detailed analysis of its trajectories in a variety of real or simulated data shows the ability of P-em to choose the most efficient paths to the global maximum of the likelihood. (C) 2012 Elsevier B.V. All rights reserved.
Finite mixture of regression (FMR) models can be reformulated as incomplete data problems and they can be estimated via the expectation-maximization (em) algorithm. The main drawback is the strong parametric assumptio...
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Finite mixture of regression (FMR) models can be reformulated as incomplete data problems and they can be estimated via the expectation-maximization (em) algorithm. The main drawback is the strong parametric assumption such as FMR models with normal distributed residuals. The estimation might be biased if the model is misspecified. To relax the parametric assumption about the component error densities, anew method is proposed to estimate the mixture regression parameters by only assuming that the components have log-concave error densities but the specific parametric family is unknown. Two em-type algorithms for the mixtures of regression models with log-concave error densities are proposed. Numerical studies are made to compare the performance of our algorithms with the normal mixture em algorithms. When the component error densities are not normal, the new methods have much smaller MSEs when compared with the standard normal mixture em algorithms, When the underlying component error densities are normal, the new methods have comparable performance to the normal em algorithm. (C) 2017 Elsevier B.V. All rights reserved.
We consider state space models where the observations are multicategorical and longitudinal, and the state is described by CHARN models. We estimate the state by generalized Kalman recursions, which rely on a variety ...
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We consider state space models where the observations are multicategorical and longitudinal, and the state is described by CHARN models. We estimate the state by generalized Kalman recursions, which rely on a variety of particle filters and em algorithm. Our results are applied to estimating the latent trait in quality of life, and this furnishes an alternative and a generalization of existing methods. These results are illustrated by numerical simulations and an application to real data in the quality of life of patients surged for breast cancer.
Regression analysis is undoubtedly an important tool to understand the relationship between one or more explanatory and independent variables of interest. In this thesis, we explore a novel methodology for fitting a w...
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Regression analysis is undoubtedly an important tool to understand the relationship between one or more explanatory and independent variables of interest. In this thesis, we explore a novel methodology for fitting a wide range of parametric and nonparametric regression models, called the I-prior methodology (Bergsma, 2018). We assume that the regression function belongs to a reproducing kernel Hilbert or Kreĭn space of functions, and by doing so, allows us to utilise the convenient topologies of these vector spaces. This is important for the derivation of the Fisher information of the regression function, which might be infinite dimensional. Based on the principle of maximum entropy, an I-prior is an objective Gaussian process prior for the regression function with covariance function proportional to its Fisher information. Our work focusses on the statistical methodology and computational aspects of fitting I-priors models. We examine a likelihood-based approach (direct optimisation and em algorithm) for fitting I-prior models with normally distributed errors. The culmination of this work is the R package iprior (Jamil, 2017) which has been made publicly available on CRAN. The normal I-prior methodology is subsequently extended to fit categorical response models, achieved by "squashing" the regression functions through a probit sigmoid function. Estimation of I-probit models, as we call it, proves challenging due to the intractable integral involved in computing the likelihood. We overcome this difficulty by way of variational approximations. Finally, we turn to a fully Bayesian approach of variable selection using I-priors for linear models to tackle multicollinearity. We illustrate the use of I-priors in various simulated and real-data examples. Our study advocates the I-prior methodology as being a simple, intuitive, and comparable alternative to similar leading state-of-the-art models.
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