Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Regularized Analysis of Large p, Small n Data (5)
- Bayesian Model Averaging and Semiparametric Regression (4)
- Forecasting and Time Series (2)
- Multivariate Models in Marketing (2)
- Working Papers (2)
-
- Order Statistics Model. (1)
- Acquired immunodeficiency syndrome (AIDS) (1)
- Asymmetric information (1)
- Backcalculation (1)
- Bayesian (1)
- Bayesian analysis (1)
- Bioinformatics/Cancer Genomics (1)
- Conference (1)
- Confidence intervals (1)
- Copula Modeling (1)
- Deconvolution (1)
- District Demand Heating (1)
- EM algorithm (1)
- Electricity Demand (1)
- Empirical Finance (1)
- Exploratory factor analysis (1)
- Extreme Value Theory (1)
- General Biostatistics (1)
- Heterogeneity model (1)
- High-frequency (1)
- Integrative analysis (1)
- Jacobian (1)
- Jeffreys Priors (1)
- Marker identification (1)
- Market microstructure (1)
Articles 1 - 30 of 40
Full-Text Articles in Physical Sciences and Mathematics
Promoting Similarity Of Model Sparsity Structures In Integrative Analysis Of Cancer Genetic Data, Shuangge Ma
Promoting Similarity Of Model Sparsity Structures In Integrative Analysis Of Cancer Genetic Data, Shuangge Ma
Shuangge Ma
In profiling studies, the analysis of a single dataset often leads to unsatisfactory results because of the small sample size. Multi-dataset analysis utilizes information across multiple independent datasets and outperforms single-dataset analysis. Among the available multi-dataset analysis methods, integrative analysis methods aggregate and analyze raw data and outperform meta-analysis methods, which analyze multiple datasets separately and then pool summary statistics. In this study, we conduct integrative analysis and marker selection under the heterogeneity structure, which allows different datasets to have overlapping but not necessarily identical sets of markers. Under certain scenarios, it is reasonable to expect some similarity of identified …
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 …
Variances For Maximum Penalized Likelihood Estimates Obtained Via The Em Algorithm, Mark Segal, Peter Bacchetti, Nicholas Jewell
Variances For Maximum Penalized Likelihood Estimates Obtained Via The Em Algorithm, Mark Segal, Peter Bacchetti, Nicholas Jewell
Mark R Segal
We address the problem of providing variances for parameter estimates obtained under a penalized likelihood formulation through use of the EM algorithm. The proposed solution represents a synthesis of two existent techniques. Firstly, we exploit the supplemented EM algorithm developed in Meng and Rubin (1991) that provides variance estimates for maximum likelihood estimates obtained via the EM algorithm. Their procedure relies on evaluating the Jacobian of the mapping induced by the EM algorithm. Secondly, we utilize a result from Green (1990) that provides an expression for the Jacobian of the mapping induced by the EM algorithm applied to a penalized …
Backcalculation Of Hiv Infection Rates, Peter Bacchetti, Mark Segal, Nicholas Jewell
Backcalculation Of Hiv Infection Rates, Peter Bacchetti, Mark Segal, Nicholas Jewell
Mark R Segal
Backcalculation is an important method of reconstructing past rates of human immunodeficiency virus (HIV) infection and for estimating current prevalence of HIV infection and future incidence of acquired immunodeficiency syndrome (AIDS). This paper reviews the backcalculation techniques, focusing on the key assumptions of the method, including the necessary information regarding incubation, reporting delay, and models for the infection curve. A summary is given of the extent to which the appropriate external information is available and whether checks of the relevant assumptions are possible through use of data on AIDS incidence from surveillance systems. A likelihood approach to backcalculation is described …
Bayesian Mixtures Of Autoregressive Models, Sally Wood, Ori Rosen, Robert Kohn
Bayesian Mixtures Of Autoregressive Models, Sally Wood, Ori Rosen, Robert Kohn
Sally Wood
In this paper we propose a class of time-domain models for analyzing possibly nonstationary time series. This class of models is formed as a mixture of time series models, whose mixing weights are a function of time. We consider specifically mixtures of autoregressive models with a common but unknown lag. The model parameters, including the number of mixture components, are estimated via Markov chain Monte Carlo methods. The methodology is illustrated with simulated and real data.
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.
Estimating Confidence Intervals For Eigenvalues In Exploratory Factor Analysis, Ross Larsen, Russell Warne
Estimating Confidence Intervals For Eigenvalues In Exploratory Factor Analysis, Ross Larsen, Russell Warne
Russell T Warne
Exploratory factor analysis (EFA) has become a common procedure in educational and psychological research. In the course of performing an EFA, researchers often base the decision of how many factors to retain on the eigenvalues for the factors. However, many researchers do not realize that eigenvalues, like all sample statistics, are subject to sampling error, which means that confidence intervals (CIs) can be estimated for each eigenvalue. In the present article, we demonstrate two methods of estimating CIs for eigenvalues: one based on the mathematical properties of the central limit theorem, and the other based on bootstrapping. References to appropriate …
Identification Of Cancer-Associated Gene Pathways From Analysis Of Expression Data, Shuangge Ma
Identification Of Cancer-Associated Gene Pathways From Analysis Of Expression Data, Shuangge Ma
Shuangge Ma
No abstract provided.
Lecture 5, Shuangge Ma
Final Project, Shuangge Ma
Lecture 4, Shuangge Ma
Lecture 4, Shuangge Ma
Computer Intensive Methods Lecture 13, Shuangge Ma
Final Project (Description), Shuangge Ma
Final Project (Data), Shuangge Ma
Lecture 3, Shuangge Ma
Lecture 2, Shuangge Ma
Reference: Multiple Imputation, Shuangge Ma
Reference: Weighted Bootstrap, Shuangge Ma
Computer Intensive Methods Lecture 9, Shuangge Ma
Computer Intensive Methods Lecture 8, Shuangge Ma
Reference: Counter Examples [Bootstrap], Shuangge Ma
Reference: Counter Examples [Bootstrap], Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 7 (Lab 2), Shuangge Ma
Computer Intensive Methods Lecture 7 (Lab 2), Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 6, Shuangge Ma
Reference: Block Jackknife, Shuangge Ma
Computer Intensive Methods Lecture 5, Shuangge Ma
Reading: Simulate Multivariate Distribution, Shuangge Ma
Reading: Simulate Multivariate Distribution, Shuangge Ma
Shuangge Ma
No abstract provided.
Computer Intensive Methods Lecture 4, Shuangge Ma