To estimate a time series model for multiple individuals, a multilevel model may be used. In this paper we compare two estimation methods for the autocorrelation in Multilevel AR(1) models, namely Maximum Likelihood Estimation (MLE) and Bayesian Markov Chain Monte Carlo. Furthermore, we examine the difference between modeling fixed and random individual parameters. To this end, we perform a sim...

The behavior of maximum likelihood estimates (MLE’s) and the likelihood ratio statistic in a family of problems involving pointwise nonparametric estimation of a monotone function is studied. This class of problems differs radically from the usual parametric or semiparametric situations in that the MLE of the monotone function at a point converges to the truth at rate n (slower than the usual √...

 In a number of life-testing experiments, there exist situations where the monitoring breaks down for a temporary period of time. In such cases, some parts of the ordered observations, for example the middle ones, are censored and the only outcomes available for analysis consist of the lower and upper order statistics. Therefore, the experimenter may not gain the complete information on fa...

The estimation of the total number of defects at early stages of the testing process helps managers to make resource allocation and deadline decisions. The use of nonbayesian approaches has proven to be accurate but presents a certain latency to achieve a reasonable accuracy. Here we describe BayesED3M , a bayesian estimator construct upon an existing MLE (Maximum Likelihood Estimator) named as...

A maximum likelihood estimation (MLE) method of high density regions in spatial point processes is introduced. The method is motivated from a network #ow approach for #exibly incorporating geometric restrictions in computing the MLEs. An easy-to-implement computational algorithm having a low order of complexity is provided. Simulation studies show that it performs very well in many di6cult situ...

To process previously JPEG coded images the knowledge of the quantization table used in compression is sometimes required. This happens for example in JPEG artifact removal and in JPEG re-compression. However, the quantization table might not be known due to various reasons. In this paper, a method is presented for the maximum likelihood estimation (MLE) of the JPEG quantization tables. An effi...

In this paper we consider the estimation of the unknown hyperparameters for the problem of reconstructing a high-resolution image from multiple undersampled, shifted, degraded frames with subpixel displacement errors. We derive mathematical expressions for the iterative calculation of the maximum likelihood estimate (mle) of the unknown hyperparameters given the low resolution observed images. ...

Alternative Estimation Procedures to OLS We have seen that OLS does a nice job in model estimation when the CLRM assumptions are met. We have also seen that violations of the CLRM lead to inefficiency and estimators that are not BLUE. We have also seen that the model y=Xβ+ε can be transformed to produce estimators that are BLUE. We now consider additional estimation techniques to least squares....

In large-scale applications of undirected graphical models, such as social networks and biological networks, similar patterns occur frequently and give rise to similar parameters. In this situation, it is beneficial to group the parameters for more efficient learning. We show that even when the grouping is unknown, we can infer these parameter groups during learning via a Bayesian approach. We ...

This paper considers the detection of a distributed target, which is important in high resolution radars (HRRs). We focus on the practical scenario where only partial observation is available. The key contribution of this work is the proposition of a generalized likelihood ratio test (GLRT) detector using matrix completion (MC). Firstly, a decision rule is obtained for the hypothesis test model...