Probability Commons^™

Machine Learning For Predictive Energy And Emissions Modeling Of Vehicles And Power Grids In The United States, S M Tanvir Faysal Alam Chowdhoury

Dissertations

The environmental benefits of electric vehicle (EV) adoption depend on more than replacing internal combustion engine vehicles with electric powertrains. EV adoption reshapes electricity demand, interacts with regional generation mixes, and influences travel behavior and congestion, creating a coupled transportation-energy system in which vehicle and power-plant emissions must be evaluated together. This dissertation develops machine-learning frameworks for predicting energy consumption and emissions from vehicles and power grids under rising EV adoption. The first component forecasts grid emissions from EV charging. Using simulation data from NREL's Cambium database, a Prophet-based time-series framework predicts carbon dioxide, nitrous oxide, and methane emission rates …

A Mechanistic Model Of The Wash Shuffle And Monte Carlo Exploration Of Its Impact On Card Shuffling In Texas Hold’Em, Michael A. Alexeev, Peter B. Chi

UNLV Gaming Research & Review Journal

In casino games using a standard deck of cards, a wash shuffle is sometimes performed prior to the rest of the card shuffling procedure. Unlike other methods of shuffling, the wash shuffle has not yet been well studied. To this end, we first develop a mechanistic model of the wash shuffle based on our observation of how cards tend to move when a wash shuffle is being performed. Then, we use this model to simulate the card shuffling procedure used in casino poker rooms, and explore the resulting impact on where the cards are dealt in the context of Texas …

Decoding The Algorithm: The Mathematics Behind Tiktok’S Short-Form Content Success, Ashley N. Lynch

Honors Scholar Theses

Within the realm of social networks, TikTok has become the central hub for short-form video content. The network’s unique ability to capture individual preferences using predictive analytics has greatly contributed to its massive success, allowing the company to optimize its performance and content personalization. In an age where digital media have such a significant influence on society, it is essential that users develop an understanding of how social network algorithms function to make more informed online decisions. Although TikTok’s technological system is primarily undisclosed, the platform certifiably leverages several key mathematical principles within its algorithm to achieve its core goals …

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

Stock Market Volatility In The United Kingdom: Simulating Post-Covid-19 Recovery, Bala A. Dahiru, Mohammed Shuaibu, Najibullah Hassanov

CBN Journal of Applied Statistics (JAS)

This paper investigates the time it would take for the FTSE-100 index to reach its post-COVID-19 peak. The paper utilises an exponential generalised autoregressive conditional heteroscedasticity (EGARCH) model that accounts for leverage effect and asymmetries. The preferred models amongst competing variants was the Autoregressive Moving Average (ARMA)-EGARCH(2,1) specification and was used to predict daily FTSE-100 data from 5th January 2000 to 21st June 2024. The empirical exercise showed that the COVID-19-induced financial crisis negatively affected the United Kingdom’s stock market performance. The results show that the FTSE100 index could reach its post-pandemic peak around 27th August, 2024 (two months after …

Optimizing Sports Outcome Prediction Through Feature Engineering And Machine Learning, Vitor S. Freitas

Graduate Theses/Dissertations

The challenge of predicting the outcome of a team game lies in the high complexity and dynamics of the sports data. This thesis focuses on the aspect of using feature engineering and the genetic algorithm to predict the winner and the score of various sports events. Generally, it deals with how machine learning algorithms are combined with state-of-the-art feature engineering techniques in sports datasets derived from various sports disciplines. In this thesis, five different machine learning models have been applied, classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

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 …

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.

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 …

Public Acceptance Of Medical Screening Recommendations, Safety Risks, And Implied Liabilities Requirements For Space Flight Participation, Cory J. Trunkhill

Doctoral Dissertations and Master's Theses

The space tourism industry is preparing to send space flight participants on orbital and suborbital flights. Space flight participants are not professional astronauts and are not subject to the rules and guidelines covering space flight crewmembers. This research addresses public acceptance of current Federal Aviation Administration guidance and regulations as designated for civil participation in human space flight.

The research utilized an ordinal linear regression analysis of survey data to explore the public acceptance of the current medical screening recommended guidance and the regulations for safety risk and implied liability for space flight participation. Independent variables constituted participant demographic representations …

Effect Of Monetary Policy Rate On Market Interest Rates In Nigeria: A Threshold And Nardl Approach, Oluwafemi E. Awopegba, Joseph O. Afolabi, Lydia T. Adeoye, Godwin O. Akpokodje

CBN Journal of Applied Statistics (JAS)

This study examines the effect of monetary policy rate (MPR) on market interest rates in Nigeria. For parsimony, we develop two indexes called the short-term interest rate (SINT) and Lending interest rate (LINT) to represent deposit and lending rates respectively. The nonlinear autoregressive distributed lag (NARDL) and threshold regression models are adopted. The study uses monthly data from 2002:M1 to 2019:M12. The results of the threshold regression model indicate that the degree of the effect of MPR on SINT and LINT above the estimated threshold of 11 and 13 percent respectively is greater and significant than if MPR were to …

Social Dimension Of Inclusive Growth In Ecowas: Implication For Poverty Reduction, Toriola K. Anu, Goerge O. Emmanuel, Ajayi O. Felix

CBN Journal of Applied Statistics (JAS)

This study investigates the implication of the social dimension of inclusive growth on poverty reduction in Economic Community of West African States (ECOWAS) countries. It specifically examines how social indices of inclusive growth comprising of income inequality, education, and health outcomes affect poverty reduction. The study uses a panel dataset of the six (6) lower-middle income countries in ECOWAS which was analysed via panel Difference Generalised Method of Moment (D-GMM). The results show that GDP per capita exerts significant negative effect on poverty while inequality, education and health outcomes do not show significant effect on poverty. Although, the estimates of …

Effect Of Fdi Inflows On Employment Generation In Selected Ecowas Countries: Heterogeneous Panel Analysis, Timothy A. Aderemi, Olawunmi Omitogun, Bukonla G. Osisanwo

CBN Journal of Applied Statistics (JAS)

The aim of this study is to examine the effect of FDI on employment in ECOWAS sub region between 1990 and 2019. The study utilizes a panel autoregressive distributed lag model to analyse the short run and long run relationship between FDI and employment across ECOWAS sub region. In the short run, the impact of FDI on employment is negative and statistically not significant. Meanwhile, in the long run FDI has a positive and statistically significant impact on employment rate. This implies that FDI has the capacity to generate employment in countries in ECOWAS sub region. Therefore, this study recommends …

Impact Of Covid-19 Pandemic On The Nigeria Stock Market: A Sectoral Stock Prices Analysis, Peter A. Adekunle, Yakubu A. Bello, Udochukwu G. Nwachukwu

CBN Journal of Applied Statistics (JAS)

This study examines the impact of the COVID-19 pandemic on sectoral stock prices in Nigeria stock market using daily data covering from February 28, 2020 to June 26, 2020. Applying the autoregressive distributed lag (ARDL) bounds test, the study finds that COVID-19 pandemic had adverse impact on the stock market indices in the short run. Furthermore, the study documents negative response of sectoral stock prices to the pandemic while the stock prices of the banking sub-sector are the worst hit. Compared to the consumer goods, and industrial subsector indices, the speed of adjustment to long run equilibrium is faster for …

Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore

SDSU Data Science Symposium

This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it …

Application Of Randomness In Finance, Jose Sanchez, Daanial Ahmad, Satyanand Singh

Publications and Research

Brownian Motion which is also considered to be a Wiener process and can be thought of as a random walk. In our project we had briefly discussed the fluctuations of financial indices and related it to Brownian Motion and the modeling of Stock prices.

Statistical Analysis Of 2017-18 Premier League Match Statistics Using A Regression Analysis In R, Bergen Campbell

Undergraduate Theses and Capstone Projects

This thesis analyzes the correlation between a team’s statistics and the success of their performances, and develops a predictive model that can be used to forecast final season results for that team. Data from the 2017-2018 Premier League season is to be gathered and broken down within R to highlight what factors and variables are largely contributing to the success or downfall of a team. A multiple linear regression model and stepwise selection process is then used to include any factors that are significant in predicting in match results.

The predictions about the 17-18 season results based on the model …

Predictive Distributions Via Filtered Historical Simulation For Financial Risk Management, Tyson Clark

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Filtered historical simulation with an underlying GARCH process can be used as a valuable tool in VaR analysis, as it derives risk estimates that are sensitive to the distributional properties of the historical data of the produced predictive density. I examine the applications to risk analysis that filtered historical simulation can provide, as well as an interpretation of the predictive density as a poor man’s Bayesian posterior distribution. The predictive density allows us to make associated probabilistic statements regarding the results for VaR analysis, giving greater measurement of risk and the ability to maintain the optimal level of risk per …

Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels

SMU Data Science Review

In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …

An Analysis Of Equity-Linked Insurance Pricing, Clara C. Ortgies

Honors Program Theses

This comprehensive study of equity-linked insurance options will explore the pricing of certificates of deposit and life insurance options using a present value method. With this study, I will be able to construct and price various equity-linked insurance products, with a focus on life insurance, that insurance companies could then sell to prospective customers. I will use concepts and formulas based in actuarial math, probability theory, and financial engineering in order to construct, price, and analyze new equity-linked insurance products. The fundamental methodology I will use involves applying pricing theory based on the expected value of the insurance payoff present …

A Traders Guide To The Predictive Universe- A Model For Predicting Oil Price Targets And Trading On Them, Jimmie Harold Lenz

Doctor of Business Administration Dissertations

At heart every trader loves volatility; this is where return on investment comes from, this is what drives the proverbial “positive alpha.” As a trader, understanding the probabilities related to the volatility of prices is key, however if you could also predict future prices with reliability the world would be your oyster. To this end, I have achieved three goals with this dissertation, to develop a model to predict future short term prices (direction and magnitude), to effectively test this by generating consistent profits utilizing a trading model developed for this purpose, and to write a paper that anyone with …

Newsvendor Models With Monte Carlo Sampling, Ijeoma W. Ekwegh

Electronic Theses and Dissertations

Newsvendor Models with Monte Carlo Sampling by Ijeoma Winifred Ekwegh The newsvendor model is used in solving inventory problems in which demand is random. In this thesis, we will focus on a method of using Monte Carlo sampling to estimate the order quantity that will either maximizes revenue or minimizes cost given that demand is uncertain. Given data, the Monte Carlo approach will be used in sampling data over scenarios and also estimating the probability density function. A bootstrapping process yields an empirical distribution for the order quantity that will maximize the expected profit. Finally, this method will be used …

Short-Term Inflation Forecasting Models For Nigeria, Sani I. Doguwa, Sarah O. Alade

CBN Journal of Applied Statistics (JAS)

Short-term inflation forecasting is an essential component of the monetary policy projections at the Central Bank of Nigeria. This paper proposes four short-term headline inflation forecasting models using the SARIMA and SARIMAX processes and compares their performance using the pseudo-out-of-sample forecasting procedure over July 2011 to September 2013. According to the results the best forecasting performance is demonstrated by the model based on the all items CPI estimated using the SARIMAX model. This model is, therefore, recommended for use in short-term forecasting of headline inflation in Nigeria. The forecasting performance up to eight months ahead, of the models based on …

An Efficient Two Sample Capture-Recapture Model With High Recaptures, Danjuma Jibasen, Yusuf J. Adams

CBN Journal of Applied Statistics (JAS)

This paper proposed an efficient two sample capture-recapture model (Ma) with high recaptures and compared it with the existing models like the model of no factor effect (Mo), behavioral response model (Mb) and the Petersen model (Ms), using simulated data. We found that the Petersen model provides a better estimate of the population size when the observations follow a hypergeometric distribution and the population is overestimated when recapture is high. It was also found that the proposed model provides a better estimator of the population size than the existing ones when the recapture is high. This model is particularly useful …

Causal Relationship Between Stock Market Index And Exchange Rate: Evidence From Nigeria, Abdulrasheed Zubair

CBN Journal of Applied Statistics (JAS)

This paper uses Johansen’s cointegration to test for the possibility of cointegration and Granger-causality to estimate the causal relationship between stock market index and monetary indicators (exchange rate and M2) before and during the global financial crisis for Nigeria, using monthly data for the period 2001–2011. Results suggest absence of long-run relationship before and during the crisis. The Granger-causality tests show a uni-directional causality running from M2 to ASI before the crisis while during the period of the crisis there is absence of causality between the variables. This suggests that ASI show responsiveness to M2. Thus, absence of the direct …

Investigating Chaos In The Nigerian Asset And Resource Management (Arm) Discovery Fund, Ibiyinka A. Fuwape, Samuel T. Ogunjo

CBN Journal of Applied Statistics (JAS)

This paper investigates chaos in a Nigerian mutual fund, Asset and Resource Management Company Limited (ARM) for a period of eleven years. The existence of chaotic signals in the data was identified by the reconstruction of the phase space of the daily closing price of the fund and the delay time was quantified using mutual information function and the embedding dimension by the false nearest neighbours, where the values were identified to be 15 and 20 respectively. The presence of chaotic signals in the ARM data was further confirmed by the correlation dimension method which yielded a dimension of 2.2 …

Modeling The Nigerian Inflation Rates Using Periodogram And Fourier Series Analysis, Chukwuemeka O. Omekara,, Emmanuel J. Ekpenyong, Micheal P. Ekerete

CBN Journal of Applied Statistics (JAS)

This work considers the application of Periodogram and Fourier Series Analysis to model all-items monthly inflation rates in Nigeria from 2003 to 2011. The main objectives are to identify inflation cycles, fit a suitable model to the data and make forecasts of future values. To achieve these objectives, monthly all-items inflation rates for the period were obtained from the Central Bank of Nigeria (CBN) website. Periodogram and Fourier series methods of analysis are used to analyze the data. Based on the analysis, it was found that inflation cycle within the period was fifty one (51) months, which coincides with the …

Nigerian Stock Index: A Search For Optimal Garch Model Using High Frequency Data, Olaoluwa Simon Yaya

CBN Journal of Applied Statistics (JAS)

This paper attempts to fit the best Generalized Autoregressive Conditional Heteroscedastic (GARCH) model for All Share Index (ASI) of Nigerian Stock Exchange (NSE) returns. A search is made on various GARCH variants specified on the assumptions of stationarity and asymmetry. Fractionally integrated types are also considered to capture the possibility of return series having property of long range dependency. The parameter estimations are carried out on the assumptions of normality and non-normality of GARCH innovations, with models and forecasts evaluated using information criteria and loss functions respectively. Under normality assumption, Hyperbolic GARCH (HYGARCH(1,d,1)) model is selected and Integrated GARCH (IGARCH(1,1)) …

Time Series Modeling Of Nigeria External Reserves, Iheanyichukwu S. Iwueze, Eleazar C. Nwogu, Valentine U. Nlebedim

CBN Journal of Applied Statistics (JAS)

This paper discusses the levels and trend of external reserves in Nigeria. The relevance of this lies in the fact that it could help to monitor the reserves and throw early warning signal about any economic crisis. Monthly data on Nigeria external reserves for the period January 1999 to December, 2008 derived from the 2008 CBN Statistical Bulletin was analyzed using ARIMA model. Results of the analyses show that (i) the data requires logarithmic transformation to stabilize the variance and make the distribution normal (ii) the appropriate model that best describes the pattern in the transformed data is the Autoregressive- …