Kuhn-Tucker And Multiple Discrete-Continuous Extreme Value Model Estimation And Simulation In R: The Rmdcev Package,
2020
University of Saskatchewan
Kuhn-Tucker And Multiple Discrete-Continuous Extreme Value Model Estimation And Simulation In R: The Rmdcev Package, Patrick Lloyd-Smith
The R Journal
This paper introduces the package rmdcev in R for estimation and simulation of KuhnTucker demand models with individual heterogeneity. The models supported by rmdcev are the multiple-discrete continuous extreme value (MDCEV) model and Kuhn-Tucker specification common in the environmental economics literature on recreation demand. Latent class and random parameters specifications can be implemented and the models are fit using maximum likelihood estimation or Bayesian estimation. The rmdcev package also implements demand forecasting and welfare calculation for policy simulation. The purpose of this paper is to describe the model estimation and simulation framework and to demonstrate the functionalities of rmdcev using …
A Unified Algorithm For The Non-Convex Penalized Estimation: The Ncpen Package,
2020
Pepperdine University
A Unified Algorithm For The Non-Convex Penalized Estimation: The Ncpen Package, Dongshin Kim, Sangin Lee, Sunghoon Kwon
The R Journal
Various R packages have been developed for the non-convex penalized estimation but they can only be applied to the smoothly clipped absolute deviation (SCAD) or minimax concave penalty (MCP). We develop an R package, entitled ncpen, for the non-convex penalized estimation in order to make data analysts to experience other non-convex penalties. The package ncpen implements a unified algorithm based on the convex concave procedure and modified local quadratic approximation algorithm, which can be applied to a broader range of non-convex penalties, including the SCAD and MCP as special examples. Many user-friendly functionalities such as generalized information criteria, cross-validation …
User-Specified General-To-Specific And Indicator Saturation Methods,
2020
BI Norwegian Business School
User-Specified General-To-Specific And Indicator Saturation Methods, Genaro Sucarrat
The R Journal
General-to-Specific (GETS) modelling provides a comprehensive, systematic and cumulative approach to modelling that is ideally suited for conditional forecasting and counterfactual analysis, whereas Indicator Saturation (ISAT) is a powerful and flexible approach to the detection and estimation of structural breaks (e.g. changes in parameters), and to the detection of outliers. To these ends, multi path backwards elimination, single and multiple hypothesis tests on the coefficients, diagnostics tests andgoodness-of-fit measures are combined to produce a parsimonious final model. In many situations a specific model or estimator is needed, a specific set of diagnostics tests may be required, or a specific f …
Editorial,
2020
Yale University
Editorial, Michael J. Kane
The R Journal
On behalf of the editorial board, I am pleased to present Volume 12 Issue 2 of the R Journal. This is my third and final issue as the Editor-in-Chief. In the last year, we have made some substantial changes to the journal that I believe will continue to increase our capacity to support the growing data science and computational statistics communities, and continue to raise the visibility of the journal. In the last few months we recruited 10 Associate Editors and we are continuing the recruitment process. I’d like to publicly welcome our new Associate Editors, and thank each of …
Openland: Software For Quantitative Analysis And Visualization Of Land Use And Cover Change,
2020
Federal University of Mato Grosso
Openland: Software For Quantitative Analysis And Visualization Of Land Use And Cover Change, Reginal Exavier, Peter Zeilhofer
The R Journal
There is an increasing availability of spatially explicit, freely available land use and cover (LUC) time series worldwide. Because of the enormous amount of data this represents, the continuous updates and improvements in spatial and temporal resolution and category differentiation, as well as increasingly dynamic and complex changes made, manual data extraction and analysis is highly time consuming, and making software tools available to automatize LUC data assessment is becoming imperative. This paper presents a software developed in R, which combines LUC raster time series data and their transitions, calculates state-of-the-art LUC change indicators, and creates spatio-temporal visualizations, all in …
E-Rum2020: How We Turned A Physical Conference Into A Successful Virtual Event,
2020
Vanlog
E-Rum2020: How We Turned A Physical Conference Into A Successful Virtual Event, Mariachiara Fortuna, Francesca Vitalini, Mirko Signorelli, Emanuela Furfaro, Federico Marini, Gert Janssenswillen, Riccardo Porreca, Riccardo L. Rossi, Andrea Guzzo, Roberta Sirovich, Andrea Melloncelli, Lorenzo Salvi, Serena Signorelli, Filippo Chiarello
The R Journal
The European R Users Meeting 2020 (e-Rum2020) was a conference that was held virtually in June 2020. Originally, e-Rum2020 had been planned as a physical event to be held in Milano. However, the spread of the COVID-19 pandemic and the declaration of a nationwide lockdown induced the Organizing Committee to fully rethink the event, and to turn it into a live virtual conference. In this article, we describe the challenges that we encountered during the organization of e-Rum2020, and how wereacted to them. In doing so, we aim to provide future conference organizers with useful information on how to organize …
Miwqs: Multiple Imputation Using Weighted Quantile Sum Regression,
2020
Virginia Commonwealth University
Miwqs: Multiple Imputation Using Weighted Quantile Sum Regression, Paul M. Hargarten, David C. Wheeler
The R Journal
The miWQS package in the Comprehensive R Archive Network (CRAN) utilizes weighted quantile sum regression (WQS) in the multiple imputation (MI) framework. The data analyzed is a set/mixture of continuous and correlated components/chemicals that are reasonable to combine in an index and share a common outcome. These components are also interval-censored between zero and upper thresholds, or detection limits, which may differ among the components. This type of data is found in areas such as chemical epidemiological studies, sociology, and genomics. The miWQS package can be run using complete or incomplete data, which may be placed in the first quantile, …
News From The Forwards Taskforce,
2020
University of Warwick
News From The Forwards Taskforce, Heather Turner
The R Journal
Forwards is an R Foundation taskforce working to widen the participation of under represented groups in the R project and in related activities, such as the useR! conference. This report rounds up activities of the taskforce during the second half of 2020.
Farmtest: An R Package For Factor-Adjusted Robust Multiple Testing,
2020
Princeton University
Farmtest: An R Package For Factor-Adjusted Robust Multiple Testing, Koushiki Bose, Jianqing Fan, Yuan Ke, Xiaoou Pan, Wen-Xin Zhou
The R Journal
We provide a publicly available library FarmTest in the R programming system. This library implements a factor-adjusted robust multiple testing principle proposed by Fan et al. (2019) for large-scale simultaneous inference on mean effects. We use a multi-factor model to explicitly capture the dependence among a large pool of variables. Three types of factors are considered: observable, latent, and a mixture of observable and latent factors. The non-factor case, which corresponds to standard multiple mean testing under weak dependence, is also included. The library implements a series of adaptive Huber methods integrated with fast data-driven tuning schemes to estimate model …
Dataset And Evaluation Of Self-Supervised Learning For Panoramic Depth Estimation,
2020
California Polytechnic State University, San Luis Obispo
Dataset And Evaluation Of Self-Supervised Learning For Panoramic Depth Estimation, Ryan Nett
Master's Theses
Depth detection is a very common computer vision problem. It shows up primarily in robotics, automation, or 3D visualization domains, as it is essential for converting images to point clouds. One of the poster child applications is self driving cars. Currently, the best methods for depth detection are either very expensive, like LIDAR, or require precise calibration, like stereo cameras. These costs have given rise to attempts to detect depth from a monocular camera (a single camera). While this is possible, it is harder than LIDAR or stereo methods since depth can't be measured from monocular images, it has to …
Exploring Information For Quantum Machine Learning Models,
2020
University of Louisville
Exploring Information For Quantum Machine Learning Models, Michael Telahun
Electronic Theses and Dissertations
Quantum computing performs calculations by using physical phenomena and quantum mechanics principles to solve problems. This form of computation theoretically has been shown to provide speed ups to some problems of modern-day processing. With much anticipation the utilization of quantum phenomena in the field of Machine Learning has become apparent. The work here develops models from two software frameworks: TensorFlow Quantum (TFQ) and PennyLane for machine learning purposes. Both developed models utilize an information encoding technique amplitude encoding for preparation of states in a quantum learning model. This thesis explores both the capacity for amplitude encoding to provide enriched state …
Static And Dynamical Properties Of Multiferroics,
2020
University of Arkansas, Fayetteville
Static And Dynamical Properties Of Multiferroics, Sayed Omid Sayedaghaee
Graduate Theses and Dissertations
Since the silicon industrial revolution in the 1950s, a lot of effort was dedicated to the research and development activities focused on material and solid-state sciences. As a result, several cutting-edge technologies are emerging including the applications of functional materials in the design and enhancement of novel devices such as sensors, highly capable data storage media, actuators, transducers, and several other types of electronic tools. In the last two decades, a class of functional materials known as multiferroics has captured significant attention because of providing a huge potential for new designs due to possessing multiple ferroic order parameters at the …
A Social Network Analysis Of Jobs And Skills,
2020
Singapore Management University
A Social Network Analysis Of Jobs And Skills, Derrick Ming Yang Lee, Dion Wei Xuan Ang, Grace Mei Ching Pua, Lee Ning Ng, Sharon Purbowo, Eugene Wen Jia Choy, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
In this study, we analyzed job roles and skills across industries in Singapore. Using social network analysis, we identified job roles with similar required skills, and we also identified relationships between job skills. Our analysis visualizes such relationships in an intuitive way. Insights derived from our analyses are expected to assist job seekers, employers as well as recruitment agencies wanting to understand trending and required job roles and skills in today’s fast changing world.
An Update On The Computational Theory Of Hamiltonian Period Functions,
2020
University of Arkansas, Fayetteville
An Update On The Computational Theory Of Hamiltonian Period Functions, Bradley Joseph Klee
Graduate Theses and Dissertations
Lately, state-of-the-art calculation in both physics and mathematics has expanded to include the field of symbolic computing. The technical content of this dissertation centers on a few Creative Telescoping algorithms of our own design (Mathematica implementations are given as a supplement). These algorithms automate analysis of integral period functions at a level of difficulty and detail far beyond what is possible using only pencil and paper (unless, perhaps, you happen to have savant-level mental acuity). We can then optimize analysis in classical physics by using the algorithms to calculate Hamiltonian period functions as solutions to ordinary differential equations. The simple …
Identifying And Characterizing Alternative News Media On Facebook,
2020
Universidade Federal de Minas Gerais
Identifying And Characterizing Alternative News Media On Facebook, Samuel S. Guimaraes, Julia C. S. Reis, Lucas Lima, Filipe N. Ribeiro, Marisa Vasconcelos, Jisun An, Haewoon Kwak, Fabricio Benevenuto
Research Collection School Of Computing and Information Systems
As Internet users increasingly rely on social media sites to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. This situation boosted the so-called independent journalism, also known as alternative news media. Identifying and characterizing all the news pages that play an important role in news dissemination is key for understanding the news ecosystems of a country. In this work, we propose a graph-based semi-supervised method to measure the political bias of pages on most countries and show …
Social Media Analytics: A Case Study Of Singapore General Election 2020,
2020
Singapore Management University
Social Media Analytics: A Case Study Of Singapore General Election 2020, Sebastian Zhi Tao Khoo, Leong Hock Ho, Ee Hong Lee, Danston Kheng Boon Goh, Zehao Zhang, Swee Hong Ng, Haodi Qi, Kyong Jin Shim
Research Collection School Of Computing and Information Systems
The 2020 Singaporean General Election (GE2020) was a general election held in Singapore on July 10, 2020. In this study, we present an analysis on social conversations about GE2020 during the election period. We analyzed social conversations from popular platforms such as Twitter, HardwareZone, and TR Emeritus.
Variational Bayesian Inference For Crowdsourcing Predictions,
2020
Singapore Management University
Variational Bayesian Inference For Crowdsourcing Predictions, Desmond Cai, Duc Thien Nguyen, Shiau Hong Lim, Laura Wynter
Research Collection School Of Computing and Information Systems
Crowdsourcing has emerged as an effective means for performing a number of machine learning tasks such as annotation and labelling of images and other data sets. In most early settings of crowdsourcing, the task involved classification, that is assigning one of a discrete set of labels to each task. Recently, however, more complex tasks have been attempted including asking crowdsource workers to assign continuous labels, or predictions. In essence, this involves the use of crowdsourcing for function estimation. We are motivated by this problem to drive applications such as collaborative prediction, that is, harnessing the wisdom of the crowd to …
Analysis Of Online Posts To Discover Student Learning Challenges And Inform Targeted Curriculum Improvement Actions,
2020
Singapore Management University
Analysis Of Online Posts To Discover Student Learning Challenges And Inform Targeted Curriculum Improvement Actions, Michelle L. F. Cheong, Jean Y. C. Chen, Bingtian Dai
Research Collection School Of Computing and Information Systems
Past research on analysing end-of-term student feedback tend to result in only high-level course improvement suggestions, and some recent research even argued that student feedback is a poor indicator of teaching effectiveness and student learning. Our intelligent Q&A platform with machine learning prediction and engagement features allow students to ask self-directed questions and provide answers in an out-of-class informal setting. By analysing such high quality and truthful posts which represent the students’ queries and knowledge about the course content, we can better identify the exact course topics which the students face learning challenges. We have implemented our Q&A platform for …
The Spatial And Temporal Impact Of Agricultural Crop Residual Burning On Local Land Surface Temperature In Three Provinces Across China From 2015 To 2017,
2020
Huazhong Agricultural University
The Spatial And Temporal Impact Of Agricultural Crop Residual Burning On Local Land Surface Temperature In Three Provinces Across China From 2015 To 2017, Wenting Zhang, Mengmeng Yu, Qingqing He, Tianwei Wang, Lu Lin, Kai Cao, Wei Huang, Peihong Fu, Jiaxin Chen
Research Collection School Of Computing and Information Systems
China has suffered from severe crop residue burning (CRB) for a long time. As a type of biomass burning, CRB leads to a huge alteration in climate due to the emission of greenhouse gases and particulates in the atmosphere and damages to surface characteristics on land. At present, a growing body of research focuses on the impact of biomass burning (BB) (e.g., forest fire, grass fire, and CRB) on climate change from the aspect of atmospheric process. Meanwhile, a small number of research studies have started to pay attention on the damage caused by BB (e.g. forest fire) on land …
Robust, Fine-Grained Occupancy Estimation Via Combined Camera & Wifi Indoor Localization,
2020
Singapore Management University
Robust, Fine-Grained Occupancy Estimation Via Combined Camera & Wifi Indoor Localization, Anuradha Ravi, Archan Misra
Research Collection School Of Computing and Information Systems
We describe the development of a robust, accurate and practically-validated technique for estimating the occupancy count in indoor spaces, based on a combination of WiFi & video sensing. While fusing these two sensing-based inputs is conceptually straightforward, the paper demonstrates and tackles the complexity that arises from several practical artefacts, such as (i) over-counting when a single individual uses multiple WiFi devices and under-counting when the individual has no such device; (ii) corresponding errors in image analysis due to real-world artefacts, such as occlusion, and (iii) the variable errors in mapping image bounding boxes (which can include multiple possible types …
