A simple statistical estimation procedure for Monte Carlo Inversion in geophysics. II: Efficiency and Hempel's paradox

1972 ◽  
Vol 96 (1) ◽  
pp. 5-14 ◽  
Author(s):  
R. S. Anderssen ◽  
E. Seneta
Keyword(s):  
Monte Carlo ◽  
Statistical Estimation ◽  
Estimation Procedure

scholarly journals Heteroskedasticity in One-Way Error Component Probit Models

Econometrics ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 35
Author(s):  
Richard Kouamé Moussa
Keyword(s):  
Monte Carlo ◽  
Robustness Analysis ◽  
Probit Model ◽  
Estimation Procedure ◽  
Error Component ◽  
Ratio Test ◽  
Probit Models ◽  
Power Of The Test ◽  
Individual Effects ◽  
High Degree

This paper introduces an estimation procedure for a random effects probit model in presence of heteroskedasticity and a likelihood ratio test for homoskedasticity. The cases where the heteroskedasticity is due to individual effects or idiosyncratic errors or both are analyzed. Monte Carlo simulations show that the test performs well in the case of high degree of heteroskedasticity. Furthermore, the power of the test increases with larger individual and time dimensions. The robustness analysis shows that applying the wrong approach may generate misleading results except for the case where both individual effects and idiosyncratic errors are modelled as heteroskedastic.

Using Data Augmentation and Markov Chain Monte Carlo for the Estimation of Unfolding Response Models

2003 ◽  
Vol 28 (3) ◽  
pp. 195-230 ◽  
Author(s):  
Matthew S. Johnson ◽  
Brian W. Junker
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Item Response ◽  
Data Augmentation ◽  
Estimation Procedure ◽  
Model Parameters ◽  
Response Model ◽  
Response Models ◽  
Mcmc Estimation

Unfolding response models, a class of item response theory (IRT) models that assume a unimodal item response function (IRF), are often used for the measurement of attitudes. Verhelst and Verstralen (1993) and Andrich and Luo (1993) independently developed unfolding response models by relating the observed responses to a more common monotone IRT model using a latent response model (LRM; Maris, 1995 ). This article generalizes their approach, and suggests a data augmentation scheme for the estimation of any unfolding response model. The article introduces two Markov chain Monte Carlo (MCMC) estimation procedures for the Bayesian estimation of unfolding model parameters; one is a direct implementation of MCMC, and the second utilizes the data augmentation method. We use the estimation procedure to analyze three data sets, one simulated, and two from real attitudinal surveys.

How Many Monte Carlo Simulations Does One Need to Do?

2005 ◽  
Vol 23 (6) ◽  
pp. 405-427 ◽  
Author(s):  
Ian Lerche ◽  
Brett S. Mudford
Keyword(s):  
Monte Carlo ◽  
Computer Program ◽  
Estimation Procedure ◽  
Computer Time ◽  
Mean Value ◽  
Numerical Examples ◽  
Unknown Number ◽  
Cumulative Probabilities ◽  
Complex Computer ◽  
Desirable Trait

This article derives an estimation procedure to evaluate how many Monte Carlo realizations need to be done in order to achieve prescribed accuracies in the estimated mean value and also in the cumulative probabilities of achieving values greater than, or less than, a particular value as the chosen particular value is allowed to vary. In addition, by inverting the argument and asking what the accuracies are that result for a prescribed number of Monte Carlo realizations, one can assess the computer time that would be involved should one choose to carry out the Monte Carlo realizations. These two complementary procedures are of great benefit in attempting to control the worth of undertaking an unknown number of Monte Carlo realizations, and of continuing to carry out the unknown number until the results have reached a level of accuracy that one deems acceptable. Such a procedure is not only computer intensive; however, is also very open-ended, a less than desirable trait when running a complex computer program that might take many hours or days to run through even once. The procedure presented here allows one to assess, ahead of performing a large number of Monte Carlo realizations, roughly how many are actually needed. Several illustrative numerical examples provide indications how one uses this novel procedure in practical situations.

scholarly journals The Minimum Inhibitory Concentration of Antibiotics: Methods, Interpretation, Clinical Relevance

Pathogens ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 165
Author(s):  
Beata Kowalska-Krochmal ◽  
Ruth Dudek-Wicher
Keyword(s):  
Monte Carlo ◽  
Bacterial Infections ◽  
Statistical Estimation ◽  
Therapeutic Strategy ◽  
Inhibitory Concentration ◽  
Therapeutic Strategies ◽  
Bacterial Strains ◽  
Method Choice ◽  
Reliable Assessment

Inefficiency of medical therapies used in order to cure patients with bacterial infections requires not only to actively look for new therapeutic strategies but also to carefully select antibiotics based on variety of parameters, including microbiological. Minimal inhibitory concentration (MIC) defines in vitro levels of susceptibility or resistance of specific bacterial strains to applied antibiotic. Reliable assessment of MIC has a significant impact on the choice of a therapeutic strategy, which affects efficiency of an infection therapy. In order to obtain credible MIC, many elements must be considered, such as proper method choice, adherence to labeling rules, and competent interpretation of the results. In this paper, two methods have been discussed: dilution and gradient used for MIC estimation. Factors which affect MIC results along with the interpretation guidelines have been described. Furthermore, opportunities to utilize MIC in clinical practice, with pharmacokinetic /pharmacodynamic parameters taken into consideration, have been investigated. Due to problems related to PK determination in individual patients, statistical estimation of the possibility of achievement of the PK/PD index, based on the Monte Carlo, was discussed. In order to provide comprehensive insights, the possible limitations of MIC, which scientists are aware of, have been outlined.

scholarly journals On the Statistical Estimation of Costs of Claims

1974 ◽  
Vol 7 (3) ◽  
pp. 181-191
Author(s):  
B. Ajne
Keyword(s):  
Data Processing ◽  
Life Insurance ◽  
Statistical Estimation ◽  
Estimation Procedure ◽  
Electronic Data Processing ◽  
Insurance Company ◽  
Total Cost ◽  
Discount Factors ◽  
Arbitrary Period ◽  
Made In

For a number of reasons it is important for an insurance company to estimate the claims costs of a year within the different branches of non-life insurance as soon as possible after the end of the year. The claims cost of a year is hereby defined as the total cost, before taking reinsurance into account, of all claims generated by events that have occurred during the year. When the estimation has to be done, part of these claims will be reported and closed, others will be reported and still open, and the remaining ones will be incurred but not yet reported. The total cost of the claims is defined as the sum of all payments that have been made or will be made on account of the claims. Thus, in this definition no regard is paid to interest, i.e. no discount factors are applied to payments to be made in the future.Instead of considering a year, we could consider an arbitrary period of twelve consecutive months. The estimation problem is the same, and estimates of the claims costs of consecutive twelve months periods will allow a closer following up of trends and yield predictions for the present year.For estimates to be available quickly, it is necessary that the estimation procedure be founded on data that are available immediately at the end of the year or the latest twelve months period. This means a.o. that for the bulk of the open claims, individual estimates of reserves by claims adjusters are out of the question. In other words, the estimation procedure has to be basically of a statistical character. In addition, for continuous estimates to be produced it has to be well adapted to electronic data processing. Indata to the procedure have to be stored in the memories of the computer.

Analyzing Marketing Research Data with Incomplete Information on the Dependent Variable

1987 ◽  
Vol 24 (1) ◽  
pp. 74-84 ◽  
Author(s):  
Naresh K. Malhotra
Keyword(s):  
Monte Carlo ◽  
Em Algorithm ◽  
Incomplete Data ◽  
Marketing Research ◽  
Estimation Procedure ◽  
Research Data ◽  
Important Class ◽  
The Em Algorithm ◽  
Monte Carlo Studies ◽  
Simple Estimation

The author presents the general EM algorithm for analyzing incomplete data. As a specific application of the EM algorithm, a model is proposed for analyzing incomplete data in an important class of problems in marketing research. A simple estimation procedure also is developed. The model is investigated through Monté Carlo studies as well as empirically with encouraging results. Some of the advantages and limitations of the approach are discussed.

Sign in / Sign up

Export Citation Format

Share Document