scholarly journals Substructure in Rich Clusters

1988 ◽  
Vol 130 ◽  
pp. 537-537
Author(s):  
Rachel Webster ◽  
Michael Fitchett ◽  
Paul Hewett ◽  
Matthew Colless
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Statistical Method

We have developed a new statistical method based on maximum likelihood to test for the existence of two subclumps in data of arbitrary dimensionality (Fitchett 1987). The statistic, which is called the Lee function, can be calibrated using Monte Carlo simulations under various null hypotheses.

scholarly journals Maximum Likelihood and Two-Step Estimation of an Ordered-Probit Selection Model

2007 ◽  
Vol 7 (2) ◽  
pp. 167-182 ◽  
Author(s):  
Richard Chiburis ◽  
Michael Lokshin
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Regression Model ◽  
Selection Rule ◽  
Ordered Probit ◽  
Maximum Likelihood Estimates ◽  
Full Information ◽  
Full Information Maximum Likelihood ◽  
Two Step Estimation

We discuss the estimation of a regression model with an ordered-probit selection rule. We have written a Stata command, oheckman, that computes two-step and full-information maximum-likelihood estimates of this model. Using Monte Carlo simulations, we compare the performances of these estimators under various conditions.

Detection Based on Relaxation in MIMO Systems

2009 ◽  
pp. 308-327
Author(s):  
Joakim Jaldén ◽  
Björn Ottersten
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Fading Channel ◽  
Error Probability ◽  
Mimo Systems ◽  
Diversity Gain ◽  
Geometrical Properties ◽  
Maximum Likelihood Detector ◽  
Implementation Aspects

This chapter takes a closer look at a class of MIMO detention methods, collectively referred to as relaxation detectors. These detectors provide computationally advantageous alternatives to the optimal maximum likelihood detector. Previous analysis of relaxation detectors have mainly focused on the implementation aspects, while resorting to Monte Carlo simulations when it comes to investigating their performance in terms of error probability. The objective of this chapter is to illustrate how the performance of any detector in this class can be readily quantified thought its diversity gain when applied to an i.i.d. Rayleigh fading channel, and to show that the diversity gain is often surprisingly simple to derive based on the geometrical properties of the detector.

Maximum Likelihood Estimation of Models with Beta-Distributed Dependent Variables

2001 ◽  
Vol 9 (4) ◽  
pp. 325-346 ◽  
Author(s):  
Philip Paolino
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Monte Carlo Simulations ◽  
Interest Groups ◽  
Political Science ◽  
Beta Distribution ◽  
Likelihood Estimation ◽  
Dependent Variables ◽  
Key Aspects

Research in political science is often concerned with modeling dependent variables that are proportions. Proportions are relevant in a wide variety of substantive areas, including elections, the bureaucracy, and interest groups. Yet because most researchers rely upon an approach, OLS, that does not recognize key aspects of proportions, the conclusions we reach from normal models may not provide the best understanding of phenomena of interest in these areas. In this paper, I use Monte Carlo simulations to show that maximum likelihood estimation of these data using the beta distribution may provide more accurate and more precise results. I then present empirical analyses illustrating some of these differences.

scholarly journals Inferences on A Normal Mean with an Auxiliary Variable

2021 ◽  
Vol 25 (2) ◽  
pp. 51-59
Author(s):  
Jianqi Yu ◽  
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Monte Carlo Simulations ◽  
Hypothesis Testing ◽  
Likelihood Estimation ◽  
Auxiliary Variable ◽  
Confidence Estimation ◽  
Normal Mean ◽  
The Mean

Inferential procedures for a normal mean with an auxiliary variable are developed. First, the maximum likelihood estimation of the mean and its distribution are derived. Second, an F statistic based on the maximum likelihood estimation is proposed, and the hypothesis testing and confidence estimation are outlined. Finally, to illustrate the advantage of using auxiliary variable, Monte Carlo simulations are performed. The results indicate that using auxiliary variable can improve the efficiency of inference.

A METHOD FOR MEASURING ELLIPTIC FLOW FLUCTUATIONS WITH THE PHOBOS DETECTOR

2007 ◽  
Vol 16 (07n08) ◽  
pp. 1852-1858
Author(s):  
◽  
BURAK ALVER
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Elliptic Flow ◽  
Monte Carlo Event ◽  
Event Generator ◽  
Analysis Method ◽  
Statistical Fluctuations ◽  
Flow Fluctuations ◽  
Dynamical Fluctuations

We have performed the first measurement of elliptic flow (v2) fluctuations in nucleus-nucleus collisions. In this paper, we describe the analysis method we have developed for this measurement. In this method, v2 is determined event-by-event by a maximum likelihood fit. The non-statistical fluctuations are determined by unfolding the contribution of statistical fluctuations and detector effects using Monte Carlo simulations. Application of this method to measure dynamical fluctuations in events from a different Monte Carlo event generator is presented.

scholarly journals Statistical Inference for the Inverted Scale Family under General Progressive Type-II Censoring

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 731
Author(s):  
Jing Gao ◽  
Kehan Bai ◽  
Wenhao Gui
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Maximum Likelihood Estimator ◽  
Confidence Intervals ◽  
Interval Estimation ◽  
Bayesian Estimator ◽  
Small Samples ◽  
Likelihood Estimator ◽  
Scale Family

Two estimation problems are studied based on the general progressively censored samples, and the distributions from the inverted scale family (ISF) are considered as prospective life distributions. One is the exact interval estimation for the unknown parameter θ , which is achieved by constructing the pivotal quantity. Through Monte Carlo simulations, the average 90 % and 95 % confidence intervals are obtained, and the validity of the above interval estimation is illustrated with a numerical example. The other is the estimation of R = P ( Y < X ) in the case of ISF. The maximum likelihood estimator (MLE) as well as approximate maximum likelihood estimator (AMLE) is obtained, together with the corresponding R-symmetric asymptotic confidence intervals. With Bootstrap methods, we also propose two R-asymmetric confidence intervals, which have a good performance for small samples. Furthermore, assuming the scale parameters follow independent gamma priors, the Bayesian estimator as well as the HPD credible interval of R is thus acquired. Finally, we make an evaluation on the effectiveness of the proposed estimations through Monte Carlo simulations and provide an illustrative example of two real datasets.

scholarly journals When Was Westerhus Churchyard in Use?

2021 ◽  
Vol 13 (1) ◽  
pp. 161-182
Author(s):  
Claes-Henric Siven
Keyword(s):  
Monte Carlo ◽  
Maximum Likelihood ◽  
Monte Carlo Simulations ◽  
Maximum Likelihood Estimator ◽  
Maximum Likelihood Method ◽  
Likelihood Method ◽  
Likelihood Estimator ◽  
Raw Data ◽  
Mass Graves ◽  
Point Estimates

The period of use for the Swedish medieval churchyard of Westerhus has been estimated by the maximum likelihood method. Raw data consist of 30 calibrated '4C-dates of some of the skeletons from the site. Bias and other properties of the maximum likelihood estimator are analyzed via a number of Monte Carlo simulations. The point estimates imply that the site was used in the period 1073-1356, that is, a somewhat longer period than previously assumed. The estimated length of the period of use affects the interpretation ofthe great number ofburied children. Population calculations lead to the conclusion that the six agglomerations of children's graves cannot be interpreted as mass graves.

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