Maximum A Posteriori Covariance Estimation Using a Power Inverse Wishart Prior

The estimation of the covariance matrix is an initial step in many multivariate statistical methods such as principal components analysis and factor analysis, but in many practical applications the dimensionality of the sample space is large compared to the number of samples, and the usual maximum likelihood estimate is poor. Typically, improvements are obtained by modelling or regularization. From a practical point of view, these methods are often computationally heavy and rely on approximations. As a fast substitute, we propose an easily calculable maximum a posteriori (MAP) estimator based on a new class of prior distributions generalizing the inverse Wishart prior, discuss its properties, and demonstrate the estimator on simulated and real data.Comment: 29 pages, 8 figures, 2 table

Nonparametric Regression with a Latent Time Series

In this paper we investigate a class of semiparametric models for panel datasetswhere the cross-section and time dimensions are large. Our model contains alatent time series that is to be estimated and perhaps forecasted along with anonparametric covariate effect. Our model is motivated by the need to be flexiblewith regard to functional form of covariate effects but also the need to be practicalwith regard to forecasting of time series effects. We propose estimation proceduresbased on local linear kernel smoothing; our estimators are all explicitly given. Weestablish the pointwise consistency and asymptotic normality of our estimators. Wealso show that the effects of estimating the latent time series can be ignored incertain cases.Kernel Estimation, Forecasting, Panel Data, Unit Roots

Deconvoluting preferences and errors: a model for binomial panel data

Let U be an unobserved random variable with compact support and let e_t be unobserved i.i.d. random errors also with compact support. Observe the random variables V_t, X_t, and Y_t = 1{U +d X_t+e_t < V_t}, t <= T, where d is an unknown parameter. This type of model is relevant for many stated choice experiments. It is shown that under weak assumptions on the support of U +e_t, the distributions of U and e_t as well as the unknown parameter d can be consistently estimated using a sieved maximum likelihood estimation procedure. The model is applied to simulated data and to actual data designed for assessing the willingness-to-pay for travel time savings

Deconvoluting preferences and errors: a model for binomial panel data

In many stated choice experiments researchers observe the random variables V_t , X_t , and Y_t = 1{U +δ X_t +e_t < V_t }, t ≤ T , where δ is an unknown parameter and U and e_t are unobservable random variables. We show that under weak assumptions the distributions of U and e_t and also the unknown parameter δ can be consistently estimated using a sieved maximum likelihood estimation procedure

Deconvoluting preferences and errors: a model for binomial panel data

In many stated choice experiments researchers observe the random variables V_t , X_t , and Y_t = 1{U +δ X_t +e_t < V_t }, t ≤ T , where δ is an unknown parameter and U and e_t are unobservable random variables. We show that under weak assumptions the distributions of U and e_t and also the unknown parameter δ can be consistently estimated using a sieved maximum likelihood estimation procedure

Deconvoluting preferences and errors: a model for binomial panel data

Let U be an unobserved random variable with compact support and let e_t be unobserved i.i.d. random errors also with compact support. Observe the random variables V_t, X_t, and Y_t = 1{U +d X_t+e_t < V_t}, t <= T, where d is an unknown parameter. This type of model is relevant for many stated choice experiments. It is shown that under weak assumptions on the support of U +e_t, the distributions of U and e_t as well as the unknown parameter d can be consistently estimated using a sieved maximum likelihood estimation procedure. The model is applied to simulated data and to actual data designed for assessing the willingness-to-pay for travel time savings

Comparison of CTA and Textual Feedback in Usability Testing for Malaysian Users

Usability moderators found that the concurrent think-aloud (CTA) method has some cultural limitation that impacts usability testing with Malaysian users. This gives rise to proposing a new method called textual feedback. The research question is to determine whether there are any differences in terms of usability defects found by employing the new method. Due to the high power distance, it is hypothesized that the CTA method may not be sufficient and hence a textual feedback method is recommended instead. Hence, the objective of this study is to determine if there are any differences in usability defects from the concurrent think-aloud (CTA) method (Condition 2) and textual feedback method (Condition 1) within the same group of Malaysian users. A pair-wise t-test was used, whereby users were subjected to performing usability task using both methods. Results reveal that we can reject the null hypothesis of "no difference" in feedback and therefore conclude that textual feedback reported significantly more usability defects than CTA, as the difference is positive t(208) = 4.791, p=0.01

Additive Intensity Regression Models in Corporate Default Analysis

We consider additive intensity (Aalen) models as an alternative to the multiplicative intensity (Cox) models for analyzing the default risk of a sample of rated, non- nancial U.S. rms. The setting allows for estimating and testing the signi cance of time-varying e ects. We use a variety of model checking techniques to identify misspeci cations. In our nal model we nd evidence of time-variation in the e ects of distance-to-default and short-to-long term debt, we identify interactions between distance-to-default and other covariates, and the quick ratio covariate is signi cant. None of our macroeconomic covariates are signi cant