scholarly journals Construction of Confidence Interval of the Parameter in Von Mises Distribution using Bootstrap Methods

2020 ◽  
pp. 1-7
Author(s):  
Nor Hafizah Moslim ◽  
Yong Zulina Zubairi ◽  
Abdul Ghapor Hussin ◽  
Siti Fatimah Hassan ◽  
Nurkhairany Amyra Mokhtar
Keyword(s):  
Confidence Interval ◽  
Bootstrap Method ◽  
Von Mises Distribution ◽  
Concentration Parameter ◽  
Statistical Inferences ◽  
Wide Range ◽  
Expected Length ◽  
Computer Based ◽  
Von Mises ◽  
Percentile Bootstrap

Bootstrap method is a computer-based technique for making certain kind of statistical inferences which can simplify the often intricate calculations of traditional statistical theory. Recently, bootstrapping has been widely used for the parameter estimation of linear data. In this paper, we consider bootstrapping methods in the construction of the confidence interval of concentration parameter, for the von Mises distribution. The performances of confidence interval based on percentile bootstrap, bootstrap-t and calibration bootstrap are evaluated via simulation study. The numerical results found that confidence interval based on the calibration bootstrap is good in terms of coverage probability. Meanwhile, confidence interval based on the bootstrap-t method has a shorter expected length. The confidence intervals were illustrated using daily wind direction data recorded at maximum wind speed for four stations in Malaysia. From point estimates of the concentration parameter and the respective confidence interval, we note that the method works well for a wide range of values. The implication of the study is that confidence interval of the concentration parameter can be obtained using bootstrap as it provides good estimates. Keywords: bootstrap-t; calibration bootstrap; concentration parameter; percentile bootstrap; von Mises distribution

scholarly journals A Comparison of Asymptotic and Bootstrapping Approach in Constructing Confidence Interval of the Concentration Parameter in von Mises Distribution

2019 ◽  
Vol 48 (5) ◽  
pp. 1151-1156
Author(s):  
Nor Hafizah Moslim ◽  
Yong Zulina Zubairi ◽  
Abdul Ghapor Hussin ◽  
Siti Fatimah Hassan ◽  
Nurkhairany Amyra Mokhtar
Keyword(s):  
Confidence Interval ◽  
Von Mises Distribution ◽  
Concentration Parameter ◽  
Von Mises

scholarly journals On the infinite divisibility of the von Mises distribution

1976 ◽  
Vol 22 (3) ◽  
pp. 332-342
Author(s):  
Toby Lewis
Keyword(s):  
Infinite Divisibility ◽  
Von Mises Distribution ◽  
Type Condition ◽  
Infinitely Divisible ◽  
Concentration Parameter ◽  
Von Mises

AbstractIt is shown, by use of a Bochner-type condition for infinite divisibility, that the von Mises distribution is infinitely divisible for some values of the concentration parameter k, certainly for k < 0.16.

scholarly journals On the approximation of the concentration parameter for von Mises distribution

2017 ◽  
Vol 13 (4-1) ◽  
pp. 390-393
Author(s):  
Nor Hafizah Moslim ◽  
Yong Zulina Zubairi ◽  
Abdul Ghapor Hussin ◽  
Siti Fatimah Hassan ◽  
Rossita Mohamad Yunus
Keyword(s):  
Goodness Of Fit ◽  
Von Mises Distribution ◽  
Concentration Parameter ◽  
Normal Behavior ◽  
Kolmogorov Smirnov ◽  
Asymptotic Normal ◽  
Level Of Significance ◽  
Von Mises ◽  
Two Parameters ◽  
Relationship Of

The von Mises distribution is the ‘natural’ analogue on the circle of the Normal distribution on the real line and is widely used to describe circular variables. The distribution has two parameters, namely mean direction,  and concentration parameter, κ. Solutions to the parameters, however, cannot be derived in the closed form. Noting the relationship of the κ to the size of sample, we examine the asymptotic normal behavior of the parameter. The simulation study is carried out and Kolmogorov-Smirnov test is used to test the goodness of fit for three level of significance values. The study suggests that as sample size and concentration parameter increase, the percentage of samples follow the normality assumption increase. 

An extension of the von mises distribution

1982 ◽  
Vol 11 (15) ◽  
pp. 1695-1706 ◽  
Author(s):  
E.A. Yfantis ◽  
L.E. Borgman
Keyword(s):  
Von Mises Distribution ◽  
Von Mises

Application of the bootstrap method to quantify uncertainty in seismic hazard estimates

1992 ◽  
Vol 82 (1) ◽  
pp. 104-119
Author(s):  
Michéle Lamarre ◽  
Brent Townshend ◽  
Haresh C. Shah
Keyword(s):  
Confidence Interval ◽  
Seismic Hazard ◽  
Statistical Method ◽  
Bootstrap Method ◽  
Earthquake Magnitude ◽  
Earthquake Catalog ◽  
Uncertainty Measure ◽  
Attenuation Models ◽  
The Bootstrap Method

Abstract This paper describes a methodology to assess the uncertainty in seismic hazard estimates at particular sites. A variant of the bootstrap statistical method is used to combine the uncertainty due to earthquake catalog incompleteness, earthquake magnitude, and recurrence and attenuation models used. The uncertainty measure is provided in the form of a confidence interval. Comparisons of this method applied to various sites in California with previous studies are used to confirm the validity of the method.

APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN MS. PAC-MAN GAME: A REVIEW

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Tse Guan Tan ◽  
Jason Teo
Keyword(s):  
Artificial Intelligence ◽  
Content Analysis ◽  
Video Games ◽  
Analysis Method ◽  
Engineering Economics ◽  
Game Ai ◽  
Content Analysis Method ◽  
Wide Range ◽  
Computer Based ◽  
Future Direction

AbstrakTeknik Kecerdasan Buatan (AI) berjaya digunakan dan diaplikasikan dalam pelbagai bidang, termasukpembuatan, kejuruteraan, ekonomi, perubatan dan ketenteraan. Kebelakangan ini, terdapat minat yangsemakin meningkat dalam Permainan Kecerdasan Buatan atau permainan AI. Permainan AI merujukkepada teknik yang diaplikasikan dalam permainan komputer dan video seperti pembelajaran, pathfinding,perancangan, dan lain-lain bagi mewujudkan tingkah laku pintar dan autonomi kepada karakter dalampermainan. Objektif utama kajian ini adalah untuk mengemukakan beberapa teknik yang biasa digunakandalam merekabentuk dan mengawal karakter berasaskan komputer untuk permainan Ms Pac-Man antaratahun 2005-2012. Ms Pac-Man adalah salah satu permainan yang digunakan dalam siri pertandinganpermainan diperingkat antarabangsa sebagai penanda aras untuk perbandingan pengawal autonomi.Kaedah analisis kandungan yang menyeluruh dijalankan secara ulasan dan sorotan literatur secara kritikal.Dapatan kajian menunjukkan bahawa, walaupun terdapat berbagai teknik, limitasi utama dalam kajianterdahulu untuk mewujudkan karakter permaianan Pac Man adalah kekurangan Generalization Capabilitydalam kepelbagaian karakter permainan. Hasil kajian ini akan dapat digunakan oleh penyelidik untukmeningkatkan keupayaan Generalization AI karakter permainan dalam Pasaran Permainan KecerdasanBuatan. Abstract Artificial Intelligence (AI) techniques are successfully used and applied in a wide range of areas, includingmanufacturing, engineering, economics, medicine and military. In recent years, there has been anincreasing interest in Game Artificial Intelligence or Game AI. Game AI refers to techniques applied incomputer and video games such as learning, pathfinding, planning, and many others for creating intelligentand autonomous behaviour to the characters in games. The main objective of this paper is to highlightseveral most common of the AI techniques for designing and controlling the computer-based charactersto play Ms. Pac-Man game between years 2005-2012. The Ms. Pac-Man is one of the games that used asbenchmark for comparison of autonomous controllers in a series of international Game AI competitions.An extensive content analysis method was conducted through critical review on previous literature relatedto the field. Findings highlight, although there was various and unique techniques available, the majorlimitation of previous studies for creating the Ms. Pac-Man game characters is a lack of generalizationcapability across different game characters. The findings could provide the future direction for researchersto improve the Generalization A.I capability of game characters in the Game Artificial Intelligence market.

Error-in-variables model of Malacca wind direction data with the von Mises distribution in southwest monsoon

2021 ◽  
Vol 15 (9) ◽  
pp. 471-479
Author(s):  
Nurkhairany Amyra Mokhtar ◽  
Basri Badyalina ◽  
Kerk Lee Chang ◽  
Fatin Farazh Ya'acob ◽  
Ahmad Faiz Ghazali ◽  
...  
Keyword(s):  
Wind Direction ◽  
Southwest Monsoon ◽  
Von Mises Distribution ◽  
Error In Variables ◽  
Von Mises

Early Acoustic Warning for the Onset of Acute Chest Syndrome in Sickle Cell Patients

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Brian Henry ◽  
Gardner Yost ◽  
Robert Molokie ◽  
Thomas J. Royston
Keyword(s):  
Sickle Cell ◽  
Hospital Setting ◽  
Pulmonary Infiltrate ◽  
Acute Chest Syndrome ◽  
Computational Techniques ◽  
X Ray ◽  
Wide Range ◽  
Chest X Ray ◽  
Computer Based ◽  
In Silico Models

Acute chest syndrome (ACS) is a leading cause of death for those with sickle cell disease (SCD). ACS is defined by the development of a new pulmonary infiltrate on chest X-ray, with fever and respiratory symptoms. Efforts have been made to apply various technologies in the hospital setting to provide earlier detection of ACS than X-ray, but they are expensive, increase radiation exposure to the patient, and are not technologies that are easily transferrable for home use to help with early diagnosis. We present preliminary studies on patients suggesting that acoustical measurements recorded quantitatively with contact sensors (electronic stethoscopes) and analyzed using advanced computational analysis methods may provide an earlier diagnostic indicator of the onset of ACS than is possible with current clinical practice. Novel in silico models of respiratory acoustics utilizing image-based and algorithmically developed lungs with full conducting airway trees support and help explain measured acoustic trends and provide guidance on the next steps in developing and translating a diagnostic approach. More broadly, the experimental and computational techniques introduced herein, while focused on monitoring and predicting the onset of ACS, could catalyze further advances in mobile health (mhealth)-enabled, computer-based auscultative diagnoses for a wide range of cardiopulmonary pathologies.

scholarly journals STATISTICAL MODEL FOR THE RELATIONSHIP BETWEEN MAIZE KERNEL ORIENTATION AND SEED LEAF AZIMUTH

2015 ◽  
Vol 52 (3) ◽  
pp. 359-370
Author(s):  
ADRIAN KOLLER ◽  
GUILHERME TORRES ◽  
MICHAEL BUSER ◽  
RANDY TAYLOR ◽  
BILL RAUN ◽  
...  
Keyword(s):  
Simulation Models ◽  
Circular Statistics ◽  
Von Mises Distribution ◽  
Canopy Closure ◽  
Canopy Architecture ◽  
Maize Kernel ◽  
Angular Data ◽  
Significant Yield ◽  
Von Mises ◽  
The Relationship

SUMMARYHand-planted plots of across-row-oriented corn seeds (Zeamays L.) produce highly structured leaf canopies and have shown significant yield advantage over randomly planted plots in prior studies. For further investigation of the phenomenon by simulation, the objective of this study was to develop a probabilistic model for the correlation between seed orientation and initial plant orientation. In greenhouse trials, the azimuthal orientation of kernels of four different hybrids was recorded at planting. At collar setting of the seed leaf, the orientation of the seed leaf was determined and the angular data subjected to the analytical methods of circular statistics. The results indicate that the correlation between seed azimuth and seed leaf azimuth can be described by a von Mises distribution. The probabilistic seed to seed leaf azimuth model described herein may be implemented in simulation models to investigate the effect of canopy architecture, canopy closure and light interception efficiency of corn under conditions of seed oriented planting.

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