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Articles 1 - 14 of 14
Full-Text Articles in Statistical Models
Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar
Predicting Financial Distress: A Comparison Of Survival Analysis And Decision Tree Techniques, Adrian Gepp, Kuldeep Kumar
Adrian Gepp
Financial distress and then the consequent failure of a business is usually an extremely costly and disruptive event. Statistical financial distress prediction models attempt to predict whether a business will experience financial distress in the future. Discriminant analysis and logistic regression have been the most popular approaches, but there is also a large number of alternative cutting - edge data mining techniques that can be used. In this paper, a semi-parametric Cox survival analysis model and non-parametric CART decision trees have been applied to financial distress prediction and compared with each other as well as the most popular approaches. This …
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 …
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 …
A Comparison Of Periodic Autoregressive And Dynamic Factor Models In Intraday Energy Demand Forecasting, Thomas Mestekemper, Goeran Kauermann, Michael Smith
A Comparison Of Periodic Autoregressive And Dynamic Factor Models In Intraday Energy Demand Forecasting, Thomas Mestekemper, Goeran Kauermann, Michael Smith
Michael Stanley Smith
We suggest a new approach for forecasting energy demand at an intraday resolution. Demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary time series models, with dimension equal to the number of intraday periods. These are a periodic autoregression and a dynamic factor model. We show the benefits of our approach in the forecasting of district heating demand in a steam network in Germany and aggregate electricity demand in the state of Victoria, Australia. In both studies, accounting for weather …
Rejoinder: Estimation Issues For Copulas Applied To Marketing Data, Peter Danaher, Michael Smith
Rejoinder: Estimation Issues For Copulas Applied To Marketing Data, Peter Danaher, Michael Smith
Michael Stanley Smith
Estimating copula models using Bayesian methods presents some subtle challenges, ranging from specification of the prior to computational tractability. There is also some debate about what is the most appropriate copula to employ from those available. We address these issues here and conclude by discussing further applications of copula models in marketing.
Forecasting Television Ratings, Peter Danaher, Tracey Dagger, Michael Smith
Forecasting Television Ratings, Peter Danaher, Tracey Dagger, Michael Smith
Michael Stanley Smith
Despite the state of flux in media today, television remains the dominant player globally for advertising spend. Since television advertising time is purchased on the basis of projected future ratings, and ad costs have skyrocketed, there is increasing pressure to forecast television ratings accurately. Previous forecasting methods are not generally very reliable and many have not been validated, but more distressingly, none have been tested in today’s multichannel environment. In this study we compare 8 different forecasting models, ranging from a naïve empirical method to a state-of-the-art Bayesian model-averaging method. Our data come from a recent time period, 2004-2008 in …
Curriculum Vitae, Tatiyana V. Apanasovich
Curriculum Vitae, Tatiyana V. Apanasovich
Tatiyana V Apanasovich
No abstract provided.
The 1905 Einstein Equation In A General Mathematical Analysis Model Of Quasars, Byron E. Bell
The 1905 Einstein Equation In A General Mathematical Analysis Model Of Quasars, Byron E. Bell
Byron E. Bell
No abstract provided.
A Mathematical Regression Of The U.S. Gross Private Domestic Investment 1959-2001, Byron E. Bell
A Mathematical Regression Of The U.S. Gross Private Domestic Investment 1959-2001, Byron E. Bell
Byron E. Bell
SUMMARY OF PROJECT What did I do? A study of the role the U.S. stock markets and money markets have possibly played in the Gross Private Domestic Investment (GPDI) of the United States from the year 1959 to the year 2001 and I created a Multiple Linear Regression Model (MLRM).
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
A Bayesian Approach To Bivariate Nonparametric Regression, Michael Smith, Robert Kohn
Michael Stanley Smith
No abstract provided.
Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn
Nonparametric Regression Using Bayesian Variable Selection, Michael Smith, Robert Kohn
Michael Stanley Smith
No abstract provided.
Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert
Finite Sample Performance Of Robust Bayesian Regression, Michael Smith, Sheather Simon, Kohn Robert
Michael Stanley Smith
No abstract provided.