Between “Breaking” And “Building”: The Bridge Theory Of Research Evaluation,
2022
Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China
School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China
Between “Breaking” And “Building”: The Bridge Theory Of Research Evaluation, Fang Xu, Xiaoxuan Li
Bulletin of Chinese Academy of Sciences (Chinese Version)
How to build "new standards" after breaking "Siwei" is a hot and difficult issue in the current reform of research evaluation, which urgently needs good theoretical and methodological support. In this context, this study puts forward the BRIDGE theory of research evaluation of scientific researchers' achievements, which is to integrate the reasonable elements in the quantitative evaluation based on SCI papers into the "new standard" based on peer review, so as to build a bridge between quantitative analysis and qualitative evaluation. The practical application of BRIDGE theory is expressed as "Six Steps", in which the second step "Recode" and the ...
Exploring Human-Caused Fire Occurrence Prediction,
2022
Western University
Exploring Human-Caused Fire Occurrence Prediction, Ruyi Jin
Undergraduate Student Research Internships Conference
Wildland Fire Science has become an increasingly hot topic in recent years. The goal of this report is to investigate human-caused wildland fire occurrence prediction. The two main predictors of interest are the mean value of the Fine Fuel Moisture Code (FFMC) and the month when a fire ignites. An Exploratory Data Analysis is presented first, after which we fit models to predict daily fire counts. We first consider Poisson models to fit the count data, but also attempt to fit Negative Binomial models to deal with overdispersion. We compare these models in the following ways: plotting the difference in ...
A Transformer-Based Classification System For Volcanic Seismic Signals,
2022
Western University
A Transformer-Based Classification System For Volcanic Seismic Signals, Anthony P. Rinaldi, Cindy Mora Stock, Cristián Bravo Roman, Alexander Hemming
Undergraduate Student Research Internships Conference
Monitoring volcanic events as they occur is a task that, to this day, requires significant human capital. The current process requires geologists to monitor seismographs around the clock, making it extremely labour-intensive and inefficient. The ability to automatically classify volcanic events as they happen in real-time would allow for quicker responses to these events by the surrounding communities. Timely knowledge of the type of event that is occurring can allow these surrounding communities to prepare or evacuate sooner depending on the magnitude of the event. Up until recently, not much research has been conducted regarding the potential for machine learning ...
Bias-Corrected Bagging In Active Learning With An Actuarial Application,
2022
Western University
Bias-Corrected Bagging In Active Learning With An Actuarial Application, Yangxuan Xu
Undergraduate Student Research Internships Conference
The variable annuity (VA) is a modern insurance product that offers certain guaranteed protection and tax-deferred treatment. Because of the inherent complexity of guarantees’ payoff, the closed-form solution of fair market values (FMVs) is often not available. Most insurance companies depend on Monte Carlo (MC) simulation to price the FMVs of these products, which is an extremely computational intensive and time-consuming approach. The metamodeling approach can be used to circumvent the heavy computation.
In the modeling stage, the bagged tree method has proved to outperform other parametric approaches. Also, a bias-corrected (BC) bagging model was tried and showed significant improvement ...
Investigating Distributions Of Epochs In Wildland Fire Lifetimes,
2022
Western University
Investigating Distributions Of Epochs In Wildland Fire Lifetimes, Xinlei Wang
Undergraduate Student Research Internships Conference
The objective of my research project is to explore the relationship between variables related to wildland fire and to model distributions of epochs in wildland fire lifetimes. Several distributional families are considered for modeling these epochs, including the exponential distribution, gamma distribution, Weibull distribution and continuous phase-type distribution. I explain each of these distributions in short terms and illustrate how they are fit. Visual results of my exploratory data analysis are illustrated in two parts, data visualization and data modeling, along with my interpretation of each. Since this work is preliminary, I conclude the report with a discussion on what ...
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 ...
Investigation Of Key Factors To Earthquake Insurance Take-Up Rates In Quebec And British Columbia Households And Prediction Model Building,
2022
Western University
Investigation Of Key Factors To Earthquake Insurance Take-Up Rates In Quebec And British Columbia Households And Prediction Model Building, Yongcheng Jiang
Undergraduate Student Research Internships Conference
Maintaining an adequate level of earthquake take-up rate could protect the insurance industry from systemic failure. Past research has shown that British Columbia and Quebec have significant differences in earthquake insurance take-up rate. This report investigates key factors from the structure (default options and various types) of the insurance plan and personal characteristics along with socioeconomic/demographic profiles that affect the demand for earthquake protection in the form of insurance. The report also provides a prediction model for earthquake insurance take-up rate. The results show an importance ranking of key factors of earthquake insurance take up, the most important three ...
Functional Structure Of Excess Return And Volatility,
2022
Western University
Functional Structure Of Excess Return And Volatility, Chenxi Zhao
Undergraduate Student Research Internships Conference
Capturing the relation between excess returns and volatility can help making better decisions in the stock market in terms of portfolio allocation and assets risk management. This paper takes the data of a minute-by-minute series of S&P500 from January 2009 to January 2021 as the research object and explores the best structural representation for the excess return as a function of the volatility, for a well-known index. This is implemented via regression models for volatility and excess returns. The results reveal that there’s a structural break in the relationship between the excess return and volatility based on the ...
Practical T-Test Power Analysis With R,
2022
National University of Singapore
Practical T-Test Power Analysis With R, Teck Kiang Tan
Practical Assessment, Research, and Evaluation
Power analysis based on the analytical t-test is an important aspect of a research study to determine the sample size required to detect the effect for the comparison of two means. The current paper presents a reader-friendly procedure for carrying out the t-test power analysis using the various R add-on packages. While there is a growing of R users in the academic that uses R as the base for carrying out research, there is a lack of reference that discusses both frequentist and Bayesian approaches and point out their distinct features for t-test power analysis. The practical aspects of the ...
Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model,
2022
Southern Methodist University
Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, Jaehyeon Yun
Statistical Science Theses and Dissertations
Alternating recurrent events data arise commonly in health research; examples include hospital admissions and discharges of diabetes patients; exacerbations and remissions of chronic bronchitis; and quitting and restarting smoking. Recent work has involved formulating and estimating joint models for the recurrent event times considering non-negligible event durations. However, prediction models for transition between recurrent events are lacking. We consider the development and evaluation of methods for predicting future events within these models. Specifically, we propose a tool for dynamically predicting transition between alternating recurrent events in real time. Under a flexible joint frailty model, we derive the predictive probability of ...
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 ...
The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics,
2022
Wayne State University
The Short-Term Effects Of Fine Airborne Particulate Matter And Climate On Covid-19 Disease Dynamics, El Hussain Shamsa, Kezhong Zhang
Medical Student Research Symposium
Background: Despite more than 60% of the United States population being fully vaccinated, COVID-19 cases continue to spike in a temporal pattern. These patterns in COVID-19 incidence and mortality may be linked to short-term changes in environmental factors.
Methods: Nationwide, county-wise measurements for COVID-19 cases and deaths, fine-airborne particulate matter (PM2.5), and maximum temperature were obtained from March 20, 2020 to March 20, 2021. Multivariate Linear Regression was used to analyze the association between environmental factors and COVID-19 incidence and mortality rates in each season. Negative Binomial Regression was used to analyze daily fluctuations of COVID-19 cases and ...
Adjusting Community Survey Data Benchmarks For External Factors,
2022
Southern Methodist University
Adjusting Community Survey Data Benchmarks For External Factors, Allen Miller, Nicole M. Norelli, Robert Slater, Mingyang N. Yu
SMU Data Science Review
Abstract. Using U.S. resident survey data from the National Community Survey in combination with public data from the U.S. Census and additional sources, a Voting Regressor Model was developed to establish fair benchmark values for city performance. These benchmarks were adjusted for characteristics the city cannot easily influence that contribute to confidence in local government, such as population size, demographics, and income. This adjustment allows for a more meaningful comparison and interpretation of survey results among individual cities. Methods explored for the benchmark adjustment included cluster analysis, anomaly detection, and a variety of regression techniques, including random forest ...
A Course In Data Science: R And Prediction Modeling,
2022
CUNY Queens College
A Course In Data Science: R And Prediction Modeling, Adam Kapelner
Open Educational Resources
This is a self-contained course in data science and machine learning using R. It covers philosophy of modeling with data, prediction via linear models, machine learning including support vector machines and random forests, probability estimation and asymmetric costs using logistic regression and probit regression, underfitting vs. overfitting, model validation, handling missingness and much more. There is formal instruction of data manipulation using dplyr and data.table, visualization using ggplot2 and statistical computing.
Statistical Characteristics Of High-Frequency Gravity Waves Observed By An Airglow Imager At Andes Lidar Observatory,
2022
Embry Riddle Aeronautical University - Daytona Beach
Statistical Characteristics Of High-Frequency Gravity Waves Observed By An Airglow Imager At Andes Lidar Observatory, Alan Z. Liu, Bing Cao
Publications
The long-term statistical characteristics of high-frequency quasi-monochromatic gravity waves are presented using multi-year airglow images observed at Andes Lidar Observatory (ALO, 30.3° S, 70.7° W) in northern Chile. The distribution of primary gravity wave parameters including horizontal wavelength, vertical wavelength, intrinsic wave speed, and intrinsic wave period are obtained and are in the ranges of 20–30 km, 15–25 km, 50–100 m s−1, and 5–10 min, respectively. The duration of persistent gravity wave events captured by the imager approximately follows an exponential distribution with an average duration of 7–9 min. The waves tend ...
How Environmental Change Will Impact Mosquito-Borne Diseases,
2022
The University of San Francisco
How Environmental Change Will Impact Mosquito-Borne Diseases, Arsal Khan
Master's Projects and Capstones
Mosquitos, the most lethal species throughout human history, are the most prevalent source of vector-borne diseases and therefore a major global health burden. Mosquito-borne disease incidence is expected to shift with environmental change. These changes can be predicted using species distribution models. With the wide variety of methods used for models, consensus for improving accuracy and comparability is needed. A comparative analysis of three recent modeling approaches revealed that integrating modeling techniques compensates for trade-offs associated with a singular approach. An area that represents a critical gap in our ability to predict mosquito behavior in response to changing climate factors ...
An Econometric Analysis Of Collegiate Player Performance To Create A Model For Forecasting Contributions To Team Success,
2022
Bellarmine University
An Econometric Analysis Of Collegiate Player Performance To Create A Model For Forecasting Contributions To Team Success, Evan Seely
Undergraduate Theses
At the conclusion of each basketball season, each conference selects 1st, 2nd, and sometimes 3rd all-conference teams based on player performance for that season. Often, these all-conference teams reflect biases in the media rather than evaluations based on player performance alone. The baseball statistic Wins Above Replacement, WAR, is useful in quantifying the impact of each player through the number of wins contributed to his respective team by comparing each player to a designated replacement level player. This statistic can also be applied to basketball analysis to perform a similar function as in baseball, despite a vastly ...
Increasing Perceived Realism Of Objects In A Mixed Reality Environment Using 'Diminished Virtual Reality',
2022
University of Mississippi
Increasing Perceived Realism Of Objects In A Mixed Reality Environment Using 'Diminished Virtual Reality', Logan Scott Parker
Honors Theses
With the recent explosion of popularity of virtual and mixed reality, an important question has arisen: “Is there a way to create a better blend of real and virtual worlds in a mixed reality experience?” This research attempts to determine whether a visual filter can be created and applied to virtual objects to better convince the brain into interpreting a composite of virtual and real views as one seamless view. The method devised in this thesis is being called 'Diminished Virtual Reality'. The results found in this study show that when presented with a scene composed of a combination of ...
Sparse Model Selection Using Information Complexity,
2022
University of Tennessee, Knoxville
Sparse Model Selection Using Information Complexity, Yaojin Sun
Doctoral Dissertations
This dissertation studies and uses the application of information complexity to statistical model selection through three different projects. Specifically, we design statistical models that incorporate sparsity features to make the models more explanatory and computationally efficient.
In the first project, we propose a Sparse Bridge Regression model for variable selection when the number of variables is much greater than the number of observations if model misspecification occurs. The model is demonstrated to have excellent explanatory power in high-dimensional data analysis through numerical simulations and real-world data analysis.
The second project proposes a novel hybrid modeling method that utilizes a mixture ...
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 ...