scholarly journals Parameter Estimation and Joint Confidence Regions for the Parameters of the Generalized Lindley Distribution

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Wenhao Gui ◽  
Man Chen
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Monte Carlo Simulations ◽  
Existence And Uniqueness ◽  
Confidence Regions ◽  
Lindley Distribution ◽  
Two Parameters ◽  
Inverse Moment

We deal with the problem of estimating the parameters of the generalized Lindley distribution. Besides the classical estimator, inverse moment and modified inverse estimators are proposed and their properties are investigated. A condition for the existence and uniqueness of the inverse moment and modified inverse estimators of the parameters is established. Monte Carlo simulations are conducted to compare the estimators’ performances. Two methods for constructing joint confidence regions for the two parameters are also proposed and their performances are discussed. A real example is presented to illustrate the proposed methods.

Nonlinear Crest, Trough, and Wave Height Distributions in Sea States With Double-Peaked Spectra

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Felice Arena ◽  
C. Guedes Soares
Keyword(s):  
Monte Carlo ◽  
Atlantic Ocean ◽  
Monte Carlo Simulations ◽  
Wave Height ◽  
Deep Water ◽  
Second Order ◽  
Statistical Distribution ◽  
Random Waves ◽  
Sea Waves ◽  
Two Parameters

The peak to trough distributions of nonlinear high sea waves in bimodal sea states in deep water are investigated. The statistical distribution of wave height is first analyzed by considering the Boccotti’s expression, where the parameters of the distribution are calculated for some bimodal spectra of sea states recorded in the Atlantic Ocean. The nonlinear crest and trough distributions are then obtained, particularizing for two peaked spectra the second-order Fedele and Arena expression, which depends on two parameters. The results have been finally validated by means of Monte Carlo simulations of second-order random waves with bimodal spectra.

Nonlinear Peak to Trough Distributions in Sea States With Double-Peaked Spectra

2007 ◽  
Author(s):  
Felice Arena ◽  
C. Guedes Soares
Keyword(s):  
Monte Carlo ◽  
Atlantic Ocean ◽  
Monte Carlo Simulations ◽  
Wave Height ◽  
Second Order ◽  
Statistical Distribution ◽  
Random Waves ◽  
Sea Waves ◽  
Nonlinear Random Waves ◽  
Two Parameters

The peaks to trough distributions of nonlinear high sea waves in bi-modal sea states are investigated. The statistical distribution of wave height is firstly investigated by considering the Boccotti’s distribution, where the parameters of the distribution are calculated for some bimodal spectra of sea states recorded in the Atlantic Ocean. The nonlinear crest and trough distributions are then obtained particularizing for two peaked spectra the second-order Fedele & Arena distribution, which depends upon two parameters. The results have been finally validated by means of Monte Carlo simulations of nonlinear random waves with bimodal spectra.

EVAPORATION FROM BARE ARID SOILS: PARAMETER ESTIMATION USING MONTE CARLO SIMULATIONS

2016 ◽  
Author(s):  
Mark B. Hausner ◽  
◽  
Jeremy Koonce ◽  
Marcus Berli ◽  
Michael H. Young
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Monte Carlo Simulations ◽  
Arid Soils

scholarly journals From the Help Desk: It's all about the Sampling

2002 ◽  
Vol 2 (2) ◽  
pp. 190-201 ◽  
Author(s):  
Allen McDowell ◽  
Jeff Pitblado
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Monte Carlo Simulations ◽  
Survey Data ◽  
Sampling Design ◽  
Hypothesis Tests ◽  
Complex Survey ◽  
Help Desk ◽  
Simple Hypothesis ◽  
Survey Designs

Effective estimation and inference, when the data are collected using complex survey designs, requires estimators that fully account for the sampling design. This article explores, by means of Monte Carlo simulations of the power of simple hypothesis tests, the consequences of parameter estimation and inference when naive estimators are employed with survey data.

scholarly journals Efficacy of Msplit Estimation in Displacement Analysis

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5047 ◽  
Author(s):  
Zbigniew Wiśniewski ◽  
Robert Duchnowski ◽  
Andrzej Dumalski
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Monte Carlo Simulations ◽  
Least Squares ◽  
Functional Model ◽  
Point Of View ◽  
Deformation Analysis ◽  
Optimal Assignment ◽  
Functional Models ◽  
Interesting Alternative

Sets of geodetic observations often contain groups of observations that differ from each other in the functional model (or at least in the values of its parameters). Sets of observations obtained at various measurement epochs is a practical example in such a context. From the conventional point of view, for example, in the least squares estimation, subsets in question should be separated before the parameter estimation. Another option would be application of Msplit estimation, which is based on a fundamental assumption that each observation is related to several competitive functional models. The optimal assignment of every observation to the respective functional model is automatic during the estimation process. Considering deformation analysis, each observation is assigned to several functional models, each of which is related to one measurement epoch. This paper focuses on the efficacy of the method in detecting point displacements. The research is based on example observation sets and the application of Monte Carlo simulations. The results were compared with the classical deformation analysis, which shows that the Msplit estimation seems to be an interesting alternative for conventional methods. The most promising are results obtained for disordered observation sets where the Msplit estimation reveals its natural advantage over the conventional approach.

Low-voltage EDS of magnesium ferrite Dendrites in a FEG-SEM

1996 ◽  
Vol 54 ◽  
pp. 478-479
Author(s):  
Matthew T. Johnson ◽  
Ian M. Anderson ◽  
Jim Bentley ◽  
C. Barry Carter
Keyword(s):  
Monte Carlo ◽  
Quantitative Analysis ◽  
Monte Carlo Simulations ◽  
Cross Section ◽  
Spatial Resolution ◽  
Low Voltage ◽  
Magnesium Ferrite ◽  
X Ray ◽  
Interaction Volume ◽  
Ferrite Spinel

Energy-dispersive X-ray spectrometry (EDS) performed at low (≤ 5 kV) accelerating voltages in the SEM has the potential for providing quantitative microanalytical information with a spatial resolution of ∼100 nm. In the present work, EDS analyses were performed on magnesium ferrite spinel [(MgxFe1−x)Fe2O4] dendrites embedded in a MgO matrix, as shown in Fig. 1. spatial resolution of X-ray microanalysis at conventional accelerating voltages is insufficient for the quantitative analysis of these dendrites, which have widths of the order of a few hundred nanometers, without deconvolution of contributions from the MgO matrix. However, Monte Carlo simulations indicate that the interaction volume for MgFe2O4 is ∼150 nm at 3 kV accelerating voltage and therefore sufficient to analyze the dendrites without matrix contributions.Single-crystal {001}-oriented MgO was reacted with hematite (Fe2O3) powder for 6 h at 1450°C in air and furnace cooled. The specimen was then cleaved to expose a clean cross-section suitable for microanalysis.

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