Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, 2022 Army Cyber Institute, U.S. Military Academy

#### Context-Aware Collaborative Neuro-Symbolic Inference In Internet Of Battlefield Things, Tarek Abdelzaher, Nathaniel D. Bastian, Susmit Jha, Lance Kaplan, Mani Srivastava, Venugopal Veeravalli

*ACI Journal Articles*

IoBTs must feature collaborative, context-aware, multi-modal fusion for real-time, robust decision-making in adversarial environments. The integration of machine learning (ML) models into IoBTs has been successful at solving these problems at a small scale (e.g., AiTR), but state-of-the-art ML models grow exponentially with increasing temporal and spatial scale of modeled phenomena, and can thus become brittle, untrustworthy, and vulnerable when interpreting large-scale tactical edge data. To address this challenge, we need to develop principles and methodologies for uncertainty-quantified neuro-symbolic ML, where learning and inference exploit symbolic knowledge and reasoning, in addition to, multi-modal and multi-vantage sensor data. The approach features …

Off-Policy Evaluation For Action-Dependent Non-Stationary Environments, 2022 Army Cyber Institute, U.S. Military Academy

#### Off-Policy Evaluation For Action-Dependent Non-Stationary Environments, Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro Da Silva, Emma Brunskill, Philip Thomas

*ACI Journal Articles*

Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary. This limits the application of such methods because real-world problems are often subject to changes due to external factors (passive non-stationarity), changes induced by interactions with the system itself (active non-stationarity), or both (hybrid non-stationarity). In this work, we take the first steps towards the fundamental challenge of on-policy and off-policy evaluation amidst structured changes due to active, passive, or hybrid non-stationarity. Towards this goal, we make a higher-order stationarity assumption such that non-stationarity results in changes over time, but the way …

Statistical Roles Of The G-Expectation Framework In Model Uncertainty: The Semi-G-Structure As A Stepping Stone, 2022 The University of Western Ontario

#### Statistical Roles Of The G-Expectation Framework In Model Uncertainty: The Semi-G-Structure As A Stepping Stone, Yifan Li

*Electronic Thesis and Dissertation Repository*

The G-expectation framework is a generalization of the classical probability system based on the sublinear expectation to deal with phenomena that cannot be described by a single probabilistic model. These phenomena are closely related to the long-existing concern about model uncertainty in statistics. However, the distributions and independence in the G-framework are quite different from the classical setup. These distinctions bring difficulty when applying the idea of this framework to general statistical practice. Therefore, a fundamental and unavoidable problem is how to better understand G-version concepts from a statistical perspective.

To explore this problem, this thesis establishes a new substructure …

Bayesian Estimation Of The Intensity Function Of A Non-Homogeneous Poisson Process, 2022 Jacksonville State University

#### Bayesian Estimation Of The Intensity Function Of A Non-Homogeneous Poisson Process, James Jensen

*Theses*

In this paper we explore Bayesian inference and its application to the problem of estimating the intensity function of a non-homogeneous Poisson process. These processes model the behavior of phenomena in which one or more events, known as arrivals, occur independently of one another over a certain period of time. We are concerned with the number of events occurring during particular time intervals across several realizations of the process. We show that given sufficient data, we are able to construct a piecewise-constant function which accurately estimates the mean rates on particular intervals. Further, we show that as we reduce these …

The Q-Analogue Of The Extended Generalized Gamma Distribution, 2022 Western University

#### The Q-Analogue Of The Extended Generalized Gamma Distribution, Wenhao Chen

*Undergraduate Student Research Internships Conference*

This project introduces a flexible univariate probability model referred to as the q-analogue of the Extended Generalized Gamma (or q-EGG) distribution, which encompasses the majority of the most frequently used continuous distributions, including the gamma, Weibull, logistic, type-1 and type-2 beta, Gaussian, Cauchy, Student-t and F. Closed form representations of its moments and cumulative distribution function are provided. Additionally, computational techniques are proposed for determining estimates of its parameters. Both the method of moments and the maximum likelihood approach are utilized. The effect of each parameter is also graphically illustrated. Certain data sets are modeled with q-EGG distributions; goodness of …

To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, 2022 University of Windsor

#### To Logit Or Not To Logit Data In The Unit Interval: A Simulation Study, Kayode Idris Hamzat

*Major Papers*

In this paper, we recommend a mechanism for determining whether to logit or not to logit data in the unit interval which is based on quantile estimation of data between 0 and 1. By using a simulated dataset generated from a Beta regression model, the estimated quantile for this model perform better than those based on the linear quantile regression with logit transformation.

Further, we investigate the performance of the quantile regression estimators based on the LQR and we conclude that it is better than those based on the Beta regression when the distribution is contaminated with 10% uniform numbers …

New Developments On The Estimability And The Estimation Of Phase-Type Actuarial Models, 2022 The University of Western Ontario

#### New Developments On The Estimability And The Estimation Of Phase-Type Actuarial Models, Cong Nie

*Electronic Thesis and Dissertation Repository*

This thesis studies the estimability and the estimation methods for two models based on Markov processes: the phase-type aging model (PTAM), which models the human aging process, and the discrete multivariate phase-type model (DMPTM), which can be used to model multivariate insurance claim processes.

The principal contributions of this thesis can be categorized into two areas. First, an objective measure of estimability is proposed to quantify estimability in the context of statistical models. Existing methods for assessing estimability require the subjective specification of thresholds, which potentially limits their usefulness. Unlike these methods, the proposed measure of estimability is objective. In …

How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, 2022 University of Arkansas, Fayetteville

#### How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, Sammy Ter Haar

*Information Systems Undergraduate Honors Theses*

Since the founding of computers, data scientists have been able to engineer devices that increase individuals’ opportunities to communicate with each other. In the 1990s, the internet took over with many people not understanding its utility. Flash forward 30 years, and we cannot live without our connection to the internet. The internet of information is what we called early adopters with individuals posting blogs for others to read, this was known as Web 1.0. As we progress, platforms became social allowing individuals in different areas to communicate and engage with each other, this was known as Web 2.0. As Dr. …

Aberrant Responding With Underlying Dominance And Unfolding Response Processes: Examining Model Fit And Performance Of Person-Fit Statistics, 2022 University of Arkansas, Fayetteville

#### Aberrant Responding With Underlying Dominance And Unfolding Response Processes: Examining Model Fit And Performance Of Person-Fit Statistics, Jennifer A. Reimers

*Graduate Theses and Dissertations*

Researchers have recognized that respondents may not answer items in a way that accurately reflects their attitude or trait level being measured. The resulting response data that deviates from what would be expected has been shown to have significant effects on the psychometric properties of a scale and analytical results. However, many studies that have investigated the detection of aberrant data and its effects have done so using dominance item response theory (IRT) models. It is unknown whether the impacts of aberrant data and the methodology used to identify aberrant responding when using dominance IRT models apply similarly when scales …

Advancements In Gaussian Process Learning For Uncertainty Quantification, 2022 Clemson University

#### Advancements In Gaussian Process Learning For Uncertainty Quantification, John C. Nicholson

*All Dissertations*

Gaussian processes are among the most useful tools in modeling continuous processes in machine learning and statistics. The research presented provides advancements in uncertainty quantification using Gaussian processes from two distinct perspectives. The first provides a more fundamental means of constructing Gaussian processes which take on arbitrary linear operator constraints in much more general framework than its predecessors, and the other from the perspective of calibration of state-aware parameters in computer models. If the value of a process is known at a finite collection of points, one may use Gaussian processes to construct a surface which interpolates these values to …

On Misuses Of The Kolmogorov–Smirnov Test For One-Sample Goodness-Of-Fit, 2022 University of Connecticut

#### On Misuses Of The Kolmogorov–Smirnov Test For One-Sample Goodness-Of-Fit, Anthony Zeimbekakis

*Honors Scholar Theses*

The Kolmogorov–Smirnov (KS) test is one of the most popular goodness-of-fit tests for comparing a sample with a hypothesized parametric distribution. Nevertheless, it has often been misused. The standard one-sample KS test applies to independent, continuous data with a hypothesized distribution that is completely specified. It is not uncommon, however, to see in the literature that it was applied to dependent, discrete, or rounded data, with hypothesized distributions containing estimated parameters. For example, it has been "discovered" multiple times that the test is too conservative when the parameters are estimated. We demonstrate misuses of the one-sample KS test in three …

Parametric And Reliability Estimation Of The Kumaraswamy Generalized Distribution Based On Record Values, 2022 Indian Institute of Technology Indore, Madhya Pradesh, India

#### Parametric And Reliability Estimation Of The Kumaraswamy Generalized Distribution Based On Record Values, Mohd. Arshad, Qazi J. Azhad

*Journal of Modern Applied Statistical Methods*

A general family of distributions, namely Kumaraswamy generalized family of (Kw-G) distribution, is considered for estimation of the unknown parameters and reliability function based on record data from Kw-G distribution. The maximum likelihood estimators (MLEs) are derived for unknown parameters and reliability function, along with its confidence intervals. A Bayesian study is carried out under symmetric and asymmetric loss functions in order to find the Bayes estimators for unknown parameters and reliability function. Future record values are predicted using Bayesian approach and non Bayesian approach, based on numerical examples and a monte carlo simulation.

Does The Type Of Records Affect The Estimates Of The Parameters?, 2022 Banaras Hindu University

#### Does The Type Of Records Affect The Estimates Of The Parameters?, Ayush Tripathi, Umesh Singh, Sanjay Kumar Singh

*Journal of Modern Applied Statistical Methods*

The maximum likelihood estimation of the unknown parameters of inverse Rayleigh and exponential distributions are discussed based on lower and upper records. The aim is to study the effect of the type of records on the behavior of the corresponding estimators. Mean squared errors are calculated through simulation to study the behavior of the estimators. The results shall be of interest to those situations where the data can be obtained in the form of either of the two types of records and the experimenter must decide between these two for estimation of the unknown parameters of the distribution.

Design Of Sksp-R Plan For Popular Statistical Distributions, 2022 GC University, Lahore, Pakistan

#### Design Of Sksp-R Plan For Popular Statistical Distributions, Jaffer Hussain, S. Balamurali, Muhammad Aslam

*Journal of Modern Applied Statistical Methods*

The design of a Skip-lot sampling plan of type SkSP-R is presented for time truncated life test for the Weibull, Exponentiated Weibull, and Birnbaum-Saunders lifetime distributions. The plan parameters of the SkSP-R plan under these three distributions are determined through a nonlinear optimization problem. Tables are also constructed for each distribution. The advantages of the proposed plan over the existing sampling schemes are discussed. Application of the proposed plan is explained with the help of an example. The Birnbaum-Saunders distribution is economically superior to other two distributions in terms of minimum average sample number.

Parameter Estimation Based On Double Ranked Set Samples With Applications To Weibull Distribution, 2022 Cairo University, Cairo, Egypt

#### Parameter Estimation Based On Double Ranked Set Samples With Applications To Weibull Distribution, Mohamed Abd Elhamed Sabry, Hiba Zeyada Muhammed, Mostafa Shaaban, Abd El Hady Nabih

*Journal of Modern Applied Statistical Methods*

In this paper, the likelihood function for parameter estimation based on double ranked set sampling (DRSS) schemes is introduced. The proposed likelihood function is used for the estimation of the Weibull distribution parameters. The maximum likelihood estimators (MLEs) are investigated and compared to the corresponding ones based on simple random sampling (SRS) and ranked set sampling (RSS) schemes. A Monte Carlo simulation is conducted and the absolute relative biases, mean square errors, and efficiencies are compared for the different schemes. It is found that, the MLEs based on DRSS is more efficient than MLE using SRS and RSS for estimating …

A New Goodness Of Fit Measure Based On Income Inequality Curves, 2022 Payam Noor University, Tehran, Iran

#### A New Goodness Of Fit Measure Based On Income Inequality Curves, Shahryar Mirzaei, S. M. A. Jahanshahi

*Journal of Modern Applied Statistical Methods*

This paper uses inequality-measurement techniques to assess goodness of fit in income distribution models. It exposes the shortcomings of the use of conventional goodness of fit criteria in face of the big income data and proposes a new set of metrics, based on income inequality curves. In this note, we mentioned that the distance between theoretical and empirical inequality curves can be considered as a goodness of fit criterion. We demonstrate certain advantages of this measure over the other general goodness of fit criteria. Unlike other goodness of fit measures, this criterion is bounded. It is 0 in minimum difference …

Non-Parametric Tests For Testing Of Scale Parameters, 2021 Panjab University, Chandigarh

#### Non-Parametric Tests For Testing Of Scale Parameters, Manish Goyal, Narinder Kumar

*Journal of Modern Applied Statistical Methods*

One of the fundamental problems in testing of equality of populations is of testing the equality of scale parameters. The subsequent usages for scale are dispersion, spread and variability. In this paper, we proposed non-parametric tests based on U-Statistics for the testing of equality of scale parameters. The null distribution of proposed tests is developed and its Pitman efficiency is worked out to compare proposed tests with respect to some existing tests. Simulation study is carried out to compute the asymptotic power of proposed tests. An illustrative example is also provided.

Confidence Interval For The Mean Of A Beta Distribution, 2021 Stephen F Austin State University

#### Confidence Interval For The Mean Of A Beta Distribution, Sean Rangel

*Electronic Theses and Dissertations*

Statistical inference for the mean of a beta distribution has become increasingly popular in various fields of academic research. In this study, we developed a novel statistical model from likelihood-based techniques to evaluate various confidence interval techniques for the mean of a beta distribution. Simulation studies will be implemented to compare the performance of the confidence intervals. In addition to the development and study involving confidence intervals, we will also apply the confidence intervals to real biological data that was gathered by the Department of Biology at Stephen F. Austin State University and provide recommendations on the best practice.

Comparative Study Of New And Traditional Estimators Of A New Lifetime Model, 2021 Central University of South Bihar, Bihar, India

#### Comparative Study Of New And Traditional Estimators Of A New Lifetime Model, Sandeep Kumar Maurya, Sanjay Kumar Singh, Umesh Singh

*Journal of Modern Applied Statistical Methods*

In this article, we have studied the behavior of estimators of parameter of a new lifetime model, suggested by Maurya et al. (2016), obtained by using methods of moments, maximum likelihood, maximum product spacing, least squares, weighted least squares, percentile, Cramer-von-Mises, Anderson-Darling and Right-tailed Anderson-Darling. Comparison of the estimators has been done on the basis of their mean square errors, biases, absolute and maximum absolute differences between empirical and estimated distribution function and a newly proposed criterion. We have also obtained the asymptomatic confidence interval and associated coverage probability for the parameter.

On The Extension Of Exponentiated Pareto Distribution, 2021 Cairo University, Giza, Egypt

#### On The Extension Of Exponentiated Pareto Distribution, Amal S. Hassan, Saeed Elsayed Hemeda, Said G. Nassr

*Journal of Modern Applied Statistical Methods*

In this study, an extended exponentiated Pareto distribution is proposed. Some statistical properties are derived. We consider maximum likelihood, least squares, weighted least squares and Bayesian estimators. A simulation study is implemented for investigating the accuracy of different estimators. An application of the proposed distribution to a real data is presented.