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Articles 1 - 11 of 11
Full-Text Articles in Physical Sciences and Mathematics
Flexible Penalized Regression For Functional Data...And Other Complex Data Objects, Philip T. Reiss
Flexible Penalized Regression For Functional Data...And Other Complex Data Objects, Philip T. Reiss
Philip T. Reiss
No abstract provided.
Extreme Rainfall Frequencies Over The Kennedy Space Center Complex, Adam Schnapp, John Lanicci
Extreme Rainfall Frequencies Over The Kennedy Space Center Complex, Adam Schnapp, John Lanicci
John M Lanicci
A study of extreme rainfall frequencies over the NASA Kennedy Space Center complex was accomplished using a high-density rainfall dataset from the Tropical Rainfall Measurement Mission’s observational network archive. Data from the network were gridded and analyzed to produce rainfall accumulation estimates for various return periods over the complex ranging from 1 to 100 years. Results of the analysis show that the rainfall accumulations for the 100-year return period are typically around 315 mm and 433 mm for 24-hour and 72-hour durations, respectively. These 100-year event estimates are consistent with those calculated from a longer-period archive at Titusville. Because the …
A Modified Version Of The Lewellen And Badrinath Measure Of Tobin's Q, Darrell Lee, James Tompkins
A Modified Version Of The Lewellen And Badrinath Measure Of Tobin's Q, Darrell Lee, James Tompkins
James Tompkins
Lewellen and Badrinath (1997) propose a superior method of measuring Tobin's Q. Unfortunately, their method is prone to a high percentage of missing observations and results in selecting samples of larger and more mature firms with lower Q statistics. A slight modification is proposed that preserves the appeal of their method, yet almost doubles the sample size, avoids sampling problems, and is statistically indistinguishable from their Q measure. In addition, a step in the Lewellen and Badrinath Q calculation is clarified, which was inadvertently omitted in their explanation, and, if left undone, can result in downward-biased measures of Q.
Dependency-Topic-Affects-Sentiment-Lda Model For Sentiment Analysis, Shunshun Yin, Jun Han, Yu Huang, Kuldeep Kumar
Dependency-Topic-Affects-Sentiment-Lda Model For Sentiment Analysis, Shunshun Yin, Jun Han, Yu Huang, Kuldeep Kumar
Kuldeep Kumar
Sentiment analysis tends to use automated approaches to mine the sentiment information expressed in text, such as reviews, blogs and forum discussions. As most traditional approaches for sentiment analysis are based on supervised learning models and need many labeled corpora as their training data which are not always easily obtained, various unsupervised models based on Latent Dirichlet Allocation (LDA) have been proposed for sentiment classification. In this paper, we propose a novel probabilistic modeling framework based on LDA, called Dependency-Topic-Affects-Sentiment-LDA (DTAS) model, which drops the ”bag of words” assumption and assumes that the topics of sentences in a document form …
Valuing Initial Intellectual Capital Contribution In New Ventures - A Short Technical Note, Peter Blood, Kuldeep Kumar, Sukanto Bhattacharya
Valuing Initial Intellectual Capital Contribution In New Ventures - A Short Technical Note, Peter Blood, Kuldeep Kumar, Sukanto Bhattacharya
Kuldeep Kumar
In this short research note, we add to the existing technical literature on venture valuations. We posit and numerically demonstrate a simple technique of valuing intellectual contribution to a new venture in the form of initial know-how. Such valuation is essential in many practical venture valuation situations where the sources of the intellectual and cash contributions are separate thus necessitating a rational model for a fair apportioning of equity.
Strategy Formation For Higher Education Institutions Using System Dynamics Modelling, Mridula Sahay, Kuldeep Kumar
Strategy Formation For Higher Education Institutions Using System Dynamics Modelling, Mridula Sahay, Kuldeep Kumar
Kuldeep Kumar
System Dynamics is the modeling technique used to understand the behavior of a complex system over time. It is particularly useful in long-term forecasting when several variables are interrelated with each other. System dynamics models are different from statistical models in the sense they not only provide forecast and control, but they also offer explanations and an understanding of the relationships between the dependent variable and numerous exogenous and endogenous variables. This research paper focuses on the strategy formation for quality improvement in Higher Education Institutions (HEI’s) using system dynamics models. Most HEI’s in developing countries are taking a strong …
Identifying Key Variables And Interactions In Statistical Models Of Building Energy Consumption Using Regularization, David Hsu
David Hsu
Statistical models can only be as good as the data put into them. Data about energy consumption continues to grow, particularly its non-technical aspects, but these variables are often interpreted differently among disciplines, datasets, and contexts. Selecting key variables and interactions is therefore an important step in achieving more accurate predictions, better interpretation, and identification of key subgroups for further analysis.
This paper therefore makes two main contributions to the modeling and analysis of energy consumption of buildings. First, it introduces regularization, also known as penalized regression, for principled selection of variables and interactions. Second, this approach is demonstrated by …
Negative Binomial Regerssion, 2nd Ed, 2nd Print, Errata And Comments, Joseph Hilbe
Negative Binomial Regerssion, 2nd Ed, 2nd Print, Errata And Comments, Joseph Hilbe
Joseph M Hilbe
Errata and Comments for 2nd printing of NBR2, 2nd edition. Previous errata from first printing all corrected. Some added and new text as well.
Bayesian Function-On-Function Regression For Multi-Level Functional Data, Mark J. Meyer, Brent A. Coull, Francesco Versace, Paul Cinciripini, Jeffrey S. Morris
Bayesian Function-On-Function Regression For Multi-Level Functional Data, Mark J. Meyer, Brent A. Coull, Francesco Versace, Paul Cinciripini, Jeffrey S. Morris
Jeffrey S. Morris
Medical and public health research increasingly involves the collection of more and more complex and high dimensional data. In particular, functional data|where the unit of observation is a curve or set of curves that are finely sampled over a grid -- is frequently obtained. Moreover, researchers often sample multiple curves per person resulting in repeated functional measures. A common question is how to analyze the relationship between two functional variables. We propose a general function-on-function regression model for repeatedly sampled functional data, presenting a simple model as well as a more extensive mixed model framework, along with multiple functional posterior …
Functional Regression, Jeffrey S. Morris
Functional Regression, Jeffrey S. Morris
Jeffrey S. Morris
Functional data analysis (FDA) involves the analysis of data whose ideal units of observation are functions defined on some continuous domain, and the observed data consist of a sample of functions taken from some population, sampled on a discrete grid. Ramsay and Silverman's 1997 textbook sparked the development of this field, which has accelerated in the past 10 years to become one of the fastest growing areas of statistics, fueled by the growing number of applications yielding this type of data. One unique characteristic of FDA is the need to combine information both across and within functions, which Ramsay and …
Ordinal Probit Wavelet-Based Functional Models For Eqtl Analysis, Mark J. Meyer, Jeffrey S. Morris, Craig P. Hersh, Jarret D. Morrow, Christoph Lange, Brent A. Coull
Ordinal Probit Wavelet-Based Functional Models For Eqtl Analysis, Mark J. Meyer, Jeffrey S. Morris, Craig P. Hersh, Jarret D. Morrow, Christoph Lange, Brent A. Coull
Jeffrey S. Morris
Current methods for conducting expression Quantitative Trait Loci (eQTL) analysis are limited in scope to a pairwise association testing between a single nucleotide polymorphism (SNPs) and expression probe set in a region around a gene of interest, thus ignoring the inherent between-SNP correlation. To determine association, p-values are then typically adjusted using Plug-in False Discovery Rate. As many SNPs are interrogated in the region and multiple probe-sets taken, the current approach requires the fitting of a large number of models. We propose to remedy this by introducing a flexible function-on-scalar regression that models the genome as a functional outcome. The …