scholarly journals Statistical Test for Bivariate Uniformity

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Zhenmin Chen ◽  
Tieyong Hu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Computer Science ◽  
Uniform Distribution ◽  
Statistical Test ◽  
Test Statistic ◽  
Univariate Case ◽  
Uniformity Test ◽  
Special Case

The purpose of the multidimension uniformity test is to check whether the underlying probability distribution of a multidimensional population differs from the multidimensional uniform distribution. The multidimensional uniformity test has applications in various fields such as biology, astronomy, and computer science. Such a test, however, has received less attention in the literature compared with the univariate case. A new test statistic for checking multidimensional uniformity is proposed in this paper. Some important properties of the proposed test statistic are discussed. As a special case, the bivariate statistic test is discussed in detail in this paper. The Monte Carlo simulation is used to compare the power of the newly proposed test with the distance-to-boundary test, which is a recently published statistical test for multidimensional uniformity. It has been shown that the test proposed in this paper is more powerful than the distance-to-boundary test in some cases.

A simple statistical test for the vertex ratio using Monte Carlo simulation

1985 ◽  
Vol 14 (2) ◽  
pp. 137-142 ◽  
Author(s):  
Ronald W.H. Verwer ◽  
Jaap van Pelt ◽  
Harry B.M. Uylings
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Statistical Test

scholarly journals Simulasi Monte Carlo dalam Memprediksi Penerimaan Peserta Pelatihan Dasar CPNS

2021 ◽  
pp. 134-138
Author(s):  
Faisal Roza ◽  
Sarjon Defit ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Civil Servant ◽  
Random Numbers ◽  
Basic Training ◽  
Internal Affairs

The implementation of basic training recruit (latsar) of civil servant (CPNS) at Pusat Pengembangan Sumber Daya Manusia (PPSDM) Ministry of Internal Affairs regional Bukittinggi. The leader takes decision in doing the implementation of latsar CPNS recruit in PPSDM scope regional Bukittinggi. Latsar CPNS is one of requirements to be civil servant. Therefore, it is necessary to collect data by doing observation, interview questionings with related party in the implementation of latsar CPNS recruit from 2018 to 2020. It can be predicted for the next recruit. After doing library references by reading some books and journals, the basic training recruit of CPNS sources from PPSDM regional Bukittinggi, and Monte Carlo simulation. By using Monte Carlo simulation in predicting data, it can get closer value of actual value. Based on distribution of sampling data, the method is by choosing random numbers from probability distribution to do simulation. The Monte Carlo result’s examination has got 173 participants for year 2019, 158 participants for year 2020, and 157 participants for year 2021 clearly. Although the rate of the accurate just reaches 81%, but it has been able to be recommended to help PPSDM regional Bukittinggi, Ministry of Internal Affairs in taking decision and planning for basic training recruit of CPNS for the next.

Operational Risk Evaluation of an Ice Feature Impact

1990 ◽  
Vol 112 (1) ◽  
pp. 96-101
Author(s):  
A. B. Dunwoody
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Stochastic Model ◽  
Risk Evaluation ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Offshore Structure ◽  
Simple Stochastic Model ◽  
General Method

The risk of impact by a particular ice feature in the vicinity of an offshore structure or stationary vessel is of concern during operations. A general method is presented for calculating the risk of an impact in terms of the joint probability distribution of the forecast positions and velocities of the ice feature. A simple stochastic model of the motion of an ice feature is introduced for which the joint probability distribution of ice feature position and velocity can be determined as a function of time. The risk of an impact is presented for this model of the motion of an ice feature. Predictions of the distributions of the time until impact and the drift speed upon impact are also presented and discussed. Predictions are compared against results of a Monte Carlo simulation.

Calculation of roc curves in multidimensional likelihood ratio based screening with Down's syndrome as a special case

1998 ◽  
Vol 5 (2) ◽  
pp. 57-62 ◽  
Author(s):  
S O Larsen ◽  
M Christiansen ◽  
B Nørgaard-Pedersen
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Programming Language ◽  
A Priori ◽  
Roc Curves ◽  
Likelihood Ratios ◽  
Screening Performance ◽  
Log Likelihood ◽  
Special Knowledge ◽  
Special Case

Objectives The development of algorithms and computer programs for the analysis of screening performance in situations with multiple normally (Gaussian) distributed selection markers and a priori risks depending on a stratification of the population. Methods The S-PLUS programming language was used to construct programs producing distributions of log likelihood ratios based on the Monte Carlo simulation. These distributions were used to construct programs for the calculation of roc curves, including a possible stratification of the population. Results S-PLUS programs for the analysis of screening performance are listed and described. The programs can be used without any special knowledge of S-PLUS. An example of the use of the programs is given.

Fractional Brownian modeled linear polymer chains with one dimensional Metropolis Monte Carlo simulation

2015 ◽  
Vol 36 ◽  
pp. 1560017
Author(s):  
J. P. B. Sambo ◽  
B. V. Gemao ◽  
J. B. Bornales
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Numerical Data ◽  
Polymer Chains ◽  
Linear Polymer ◽  
Hurst Parameter ◽  
Metropolis Monte Carlo ◽  
End Distance ◽  
Good Agreement

The scaling expression for fractional Brownian modeled linear polymer chains was obtained both theoretically and numerically. Through the probability distribution of fractional Brownian paths, the scaling was found out to be 〈R2〉 ~ N2H, where R is the end-to-end distance of the polymer chain, N is the number of monomer units and H is the Hurst parameter. Numerical data was generated through the use of Monte Carlo simulation implementing the Metropolis algorithm. Results show good agreement between numerical and theoretical scaling constants after some parameter optimization. The probability distribution confirmed the Gaussian nature of fractional Brownian motion and the behavior is not affected by varying values of the Hurst parameter and of the number of monomer units.

scholarly journals Corrigendum to ``Descriptive Measures of Poisson-Lomax Distribution''

2020 ◽  
Vol 43 (2) ◽  
pp. 345-353
Author(s):  
Khushnoor Khan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Probability Distribution ◽  
Programming Language ◽  
Numerical Results ◽  
Due Diligence ◽  
Mean Values ◽  
Lomax Distribution ◽  
Mathematical Properties ◽  
The Mean

This corrigendum focuses on the correction of numerical results derived from Poisson-Lomax Distribution (PLD) originally proposed by Al-Zahrani & Sagor (2014). Though the mathematical properties and derivations by Al-Zahrani & Sagor (2014) were immaculate but during the execution ofthe R codes using Monte Carlo simulation some anomalies occurred in the calculation of the mean values. The same  anomalies are addressed in thepresent corrigendum. The outcome of the corrigendum will provide basic guidelines for the academia and reviewers of various journals to match thenumerical results with the shape of the probability distribution under study. The results will also emphasize the fact that code writing is a cumbersome process and due diligence be exercised in executing the codes using any programming language. Relevant R codes are appended in Appendix 'A'.

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