Statistical Models Commons^™

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

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

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

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

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

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 …