Improving The Efficiency Of Exponential Ratio-Type Estimator For Population Median: A Calibration Weight Adjustment Approach,
2023
Department of Statistics, Akwa Ibom State University, Nigeria.
Improving The Efficiency Of Exponential Ratio-Type Estimator For Population Median: A Calibration Weight Adjustment Approach, Mathew J. Iseh, Kufre J. Bassey
CBN Journal of Applied Statistics (JAS)
This paper modifies the Bahl and Tuteja exponential ratio-type estimator for population median under simple random and stratified sampling schemes using calibration weight adjustment technique with supplementary information to vary the stratum weights. The bias and mean square error of the modified estimator were obtained up to the second-order approximation, which satisfies the necessary conditions for efficiency. The findings show that the new estimator surpasses existing estimators in efficiency gain. This suggests the appropriateness of calibration weight modification in boosting the efficiency of a population parameter estimator under stratified random sampling especially where the population parameter of the auxiliary variable …
(R2031) M/M/1 Retrial Queue With Working Vacation And Interruption In Bernoulli Schedule Under N-Control Pattern,
2023
Annamalai University
(R2031) M/M/1 Retrial Queue With Working Vacation And Interruption In Bernoulli Schedule Under N-Control Pattern, P. Manoharan, S. Pazhani Bala Murugan, A. Sobanappriya
Applications and Applied Mathematics: An International Journal (AAM)
An M/M/1 retrial queue with working vacation and interruption in Bernoulli schedule under N-control pattern is investigated in this article. To describe the system, we employ a QBD analogy. The model’s stability condition is deduced. The stationary probability distribution is generated by utilizing the matrix-analytic technique. The performance measures and special cases are designed. The model’s firmness is demonstrated numerically
(R2051) Analysis Of Map/Ph1, Ph2/2 Queueing Model With Working Breakdown, Repairs, Optional Service, And Balking,
2023
Puducherry Technological University
(R2051) Analysis Of Map/Ph1, Ph2/2 Queueing Model With Working Breakdown, Repairs, Optional Service, And Balking, G. Ayyappan, G. Archana
Applications and Applied Mathematics: An International Journal (AAM)
In this paper, a classical queueing system with two types of heterogeneous servers has been considered. The Markovian Arrival Process (MAP) is used for the customer arrival, while phase type distribution (PH) is applicable for the offering of service to customers as well as the repair time of servers. Optional service are provided by the servers to the unsatisfied customers. The server-2 may get breakdown during the busy period of any type of service. Though the server- 2 got breakdown, server-2 has a capacity to provide the service at a slower rate to the current customer who is receiving service …
(R2027) A New Class Of Pareto Distribution: Estimation And Its Applications,
2023
St. Thomas College, Palai
(R2027) A New Class Of Pareto Distribution: Estimation And Its Applications, Anitta Susan Aniyan, Dais George
Applications and Applied Mathematics: An International Journal (AAM)
The classical Pareto distribution is a positively skewed and right heavy-tailed lifetime distribution having a lot many applications in various fields of science and social science. In this work, via logarithmic trans-formed method, a new three parameter lifetime distribution, an extension of classical Pareto distribution is generated. The different structural properties of the new distribution are studied. The model parameters are estimated by the method of maximum likelihood and Bayesian procedure. When all the three parameters of the distribution are unknown, the Bayes estimators cannot be obtained in a closed form and hence, the Lindley’s approximation under squared error loss …
Payments Data In Gambling Research,
2023
University of Nevada, Las Vegas
Payments Data In Gambling Research, Kasra Ghaharian, Mana Azizsoltani
International Conference on Gambling & Risk Taking
A considerable body of gambling-related research has leveraged gamblers' behavioral tracking data to address a broad set of research questions. These data have typically comprised of gamblers' betting-related behaviors including, for example, the frequency and volume of betting. The analysis of gamblers' payment-related behavioral data is far less common, but provides a fruitful avenue gambling-related research.
In this presentation we discuss a selection of potential research opportunities that payments transaction data presents. We supplement this discussion with specific analyses that have been performed by our research group. We also discuss knowledge gaps and areas for future research.
A Game-Theoretic Analysis Of Baccara Chemin De Fer, Ii,
2023
University of Utah
A Game-Theoretic Analysis Of Baccara Chemin De Fer, Ii, Stewart N. Ethier, Jiyeon Lee
International Conference on Gambling & Risk Taking
uploaded
Tardys Quantifiers: Extracting Temporal And Reversible Dynamical Symmetries,
2023
Eastern Washington University
Tardys Quantifiers: Extracting Temporal And Reversible Dynamical Symmetries, Nhat Vu Minh Nguyen, Arjendu K. Pattanayak, Andres Aragoneses
2023 Symposium
One of the great challenges in complex and chaotic dynamics is to reveal the details of its underlying determinism. This can be manifest in the form of temporal correlations or structured patterns in the dynamics of a measurable variable. These temporal dynamical structures are sometimes a consequence of hidden global symmetries. Here we identify the temporal (approximate) symmetries of a semiconductor laser with external optical feedback, based on which we define the Temporal And Reversible DYnamical Symmetry (TARDYS) quantifiers to evaluate the relevance of specific temporal correlations in a time series. We show that these symmetries are also present in …
A Novel Family Of Chain Binomial Models To Investigate Correlated Vaccination And Infection Rates In Sveirs Epidemic Dynamics,
2023
Virginia Commonwealth University
A Novel Family Of Chain Binomial Models To Investigate Correlated Vaccination And Infection Rates In Sveirs Epidemic Dynamics, Divine Wanduku
Biology and Medicine Through Mathematics Conference
No abstract provided.
Public Acceptance Of Guidance And Regulations For Space Flight Participation,
2023
Embry-Riddle Aeronautical University
Public Acceptance Of Guidance And Regulations For Space Flight Participation, Cory Trunkhill, Robert Joslin, Joseph Keebler
Journal of Aviation Technology and Engineering
Space flight participants are not professional astronauts and not subject to the rules and guidance covering space flight crewmembers. Ordinal logistic regression of survey data was utilized to explore public acceptance of current medical screening recommendations and regulations for safety risk and implied liability for civil space flight participation. Independent variables constituted participant demographic representations while dependent variables represented current Federal Aviation Administration guidance and regulations. Odds ratios were derived based on the demographic categories to interpret likelihood of acceptance for the criteria. Significant likely acceptance of guidance and regulations was found for five of twelve demographic variables influencing public …
Mixing Measures For Trees Of Fixed Diameter,
2023
Macalester College
Mixing Measures For Trees Of Fixed Diameter, Ari Holcombe Pomerance
Mathematics, Statistics, and Computer Science Honors Projects
A mixing measure is the expected length of a random walk in a graph given a set of starting and stopping conditions. We determine the tree structures of order n with diameter d that minimize and maximize for a few mixing measures. We show that the maximizing tree is usually a broom graph or a double broom graph and that the minimizing tree is usually a seesaw graph or a double seesaw graph.
Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines And Bidirectional Encoders From Transformers,
2023
East Tennessee State University
Predicting High-Cap Tech Stock Polarity: A Combined Approach Using Support Vector Machines And Bidirectional Encoders From Transformers, Ian L. Grisham
Electronic Theses and Dissertations
The abundance, accessibility, and scale of data have engendered an era where machine learning can quickly and accurately solve complex problems, identify complicated patterns, and uncover intricate trends. One research area where many have applied these techniques is the stock market. Yet, financial domains are influenced by many factors and are notoriously difficult to predict due to their volatile and multivariate behavior. However, the literature indicates that public sentiment data may exhibit significant predictive qualities and improve a model’s ability to predict intricate trends. In this study, momentum SVM classification accuracy was compared between datasets that did and did not …
Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels,
2023
Old Dominion University
Statistical Approach To Quantifying Interceptability Of Interaction Scenarios For Testing Autonomous Surface Vessels, Benjamin E. Hargis, Yiannis E. Papelis
Modeling, Simulation and Visualization Student Capstone Conference
This paper presents a probabilistic approach to quantifying interceptability of an interaction scenario designed to test collision avoidance of autonomous navigation algorithms. Interceptability is one of many measures to determine the complexity or difficulty of an interaction scenario. This approach uses a combined probability model of capability and intent to create a predicted position probability map for the system under test. Then, intercept-ability is quantified by determining the overlap between the system under test probability map and the intruder’s capability model. The approach is general; however, a demonstration is provided using kinematic capability models and an odometry-based intent model.
On Characterization Of The Exponential Distribution Via Hypoexponential Distributions,
2023
The University of Texas Rio Grande Valley
On Characterization Of The Exponential Distribution Via Hypoexponential Distributions, George Yanev
School of Mathematical & Statistical Sciences Faculty Publications
The sum of independent, but not necessary identically distributed, exponential random variables follows a hypoexponential distribution. We focus on a particular case when all but one rate parameters of the exponential variables are identical. This is known as exponentially modified Erlang distribution in molecular biology. We prove a characterization of the exponential distribution, which complements previous characterizations via hypoexponential distribution with all rates different from each other.
Strong Recovery In Group Synchronization,
2023
University of North Carolina at Charlotte
Strong Recovery In Group Synchronization, Bradley Stich
Rose-Hulman Undergraduate Mathematics Journal
The group synchronization problem is to estimate unknown group elements at the vertices of a graph when given a set of possibly noisy observations of group differences at the edges. We consider the group synchronization problem on finite graphs with size tending to infinity, and we focus on the question of whether the true edge differences can be exactly recovered from the observations (i.e., strong recovery). We prove two main results, one positive and one negative. In the positive direction, we prove that for a sequence of synchronization problems containing the complete digraph along with a relatively well behaved prior …
The Probability Of Miracles,
2023
Gettysburg College
The Probability Of Miracles, Lewis A. Pummell
CAFE Symposium 2023
An insight into the probability that we will experience a miracle within our lives. This project considers different ways of defining a miracle, and how this impacts how we consider them in our lives. They are paradoxical, and completely subjective - although there are key concepts of probability which will guide opinion.
The Use Of Probability In Quantum Mechanics To Calculate Measurement Outcomes,
2023
Gettysburg College
The Use Of Probability In Quantum Mechanics To Calculate Measurement Outcomes, Hannah E. Collins
CAFE Symposium 2023
The concept of probability can help measure some of the possible outcomes of different experiments in the field of quantum mechanics. Those experiments include Thomas Young's double slit experiment, the Schrödinger equation, the wave function, and the Born Rule, which all make use of probability to predict the placement of certain subatomic particles including photons of light, in the experiments. In this project, the manner in which probability does this is explored in depth.
Looking For Life,
2023
Gettysburg College
Looking For Life, Conor C. Grubb
CAFE Symposium 2023
The topic of aliens is not just about conspiracy theories and tinfoil hats, through the years numerous respected scientists have weighed in and put thought into the topic. The Search for Extraterrestrial Intelligence (SETI) is closely tied to the Fermi Paradox and the Drake Equation. The Fermi Paradox considers why humans haven't already interacted with aliens if they exist, and the Drake Equation outlines potential variables that would influence the chances of humanity receiving radio contact from an alien civilization.
Finite Mixture Modeling For Hierarchically Structured Data With Application To Keystroke Dynamics,
2023
South Dakota State University
Finite Mixture Modeling For Hierarchically Structured Data With Application To Keystroke Dynamics, Andrew Simpson, Semhar Michael
SDSU Data Science Symposium
Keystroke dynamics has been used to both authenticate users of computer systems and detect unauthorized users who attempt to access the system. Monitoring keystroke dynamics adds another level to computer security as passwords are often compromised. Keystrokes can also be continuously monitored long after a password has been entered and the user is accessing the system for added security. Many of the current methods that have been proposed are supervised methods in that they assume that the true user of each keystroke is known apriori. This is not always true for example with businesses and government agencies which have internal …
An Adaptive Algorithm For `The Secretary Problem': Alternate Proof Of The Divergence Of A Maximizer Sequence,
2023
Old Dominion University
An Adaptive Algorithm For `The Secretary Problem': Alternate Proof Of The Divergence Of A Maximizer Sequence, Andrew Benfante, Xiang Xu
OUR Journal: ODU Undergraduate Research Journal
This paper presents an alternate proof of the divergence of the unique maximizer sequence {𝑥∗ 𝑛} of a function sequence {𝐹𝑛(𝑥)} that is derived from an adaptive algorithm based on the now classic optimal stopping problem, known by many names but here ‘the secretary problem’. The alternate proof uses a result established by Nguyen, Xu, and Zhao (n.d.) regarding the uniqueness of maximizer points of a generalized function sequence {𝑆𝜇,𝜎 𝑛 } and relies on the strict monotonicity of 𝐹𝑛(𝑥) as 𝑛 increases in order to show divergence of {𝑥∗ 𝑛}. Towards this, limits of the exponentiated Gaussian CDF are …
Novel Bayesian Neural Networks And Uncertainty Quantification Of Computational Mechanics Models,
2023
Michigan Technological University
Novel Bayesian Neural Networks And Uncertainty Quantification Of Computational Mechanics Models, Ponkrshnan Thiagarajan
Dissertations, Master's Theses and Master's Reports
Computational and data-driven models suffer from a wide range of uncertainties that impact the reliability of such models. Given the exponential proliferation of machine learning models in real-world systems, establishing a degree of confidence in their predictions becomes paramount. Reliability in predictions takes on utmost significance in domains such as autonomous driving, medical image analysis, etc., where human lives are involved, and inaccuracies in predictions could lead to disastrous outcomes. For these reasons, comprehending and quantifying uncertainties in computational and data-driven models is of utmost importance. A number of techniques have been developed to quantify uncertainties in machine learning models. …
