AStA Advances in Statistical Analysis
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Assessing the contribution of R&D to total factor productivity—a Bayesian approach to account for heterogeneity and heteroskedasticity
AStA Advances in Statistical Analysis - Tập 95 - Trang 435-452 - 2011
This paper proposes a hierarchical Bayes estimator for a panel data random coefficient model with heteroskedasticity to assess the contribution of R&D capital to total factor productivity. Based on Hall (1993) data for 323 US firms over 1976–1990, we find that there appear to have substantial unobserved heterogeneity and heteroskedasticity across firms and industries that support the use of our Ba...... hiện toàn bộ
Multinomial choice models based on Archimedean copulas
AStA Advances in Statistical Analysis - Tập 100 - Trang 333-354 - 2015
Multinomial choice models are used for the analysis of unordered, mutually exclusive choice alternatives. Conventionally used multinomial choice models are the multinomial logit, nested logit, multinomial probit and random parameters logit models. This paper develops multinomial choice models based on Archimedean copulas. In contrast to the multinomial logit and nested logit models, no independenc...... hiện toàn bộ
A spatial randomness test based on the box-counting dimension
AStA Advances in Statistical Analysis - Tập 106 Số 3 - Trang 499-524 - 2022
Measuring serial dependence in categorical time series
AStA Advances in Statistical Analysis - Tập 92 - Trang 71-89 - 2008
The analysis of time-indexed categorical data is important in many fields, e.g., in telecommunication network monitoring, manufacturing process control, ecology, etc. Primary interest is in detecting and measuring serial associations and dependencies in such data. For cardinal time series analysis, autocorrelation is a convenient and informative measure of serial association. Yet, for categorical ...... hiện toàn bộ
Efficient ways to impute incomplete panel data
AStA Advances in Statistical Analysis - - 2011
We find that existing multiple imputation procedures that are currently implemented in major statistical packages and that are available to the wide majority of data analysts are limited with regard to handling incomplete panel data. We review various missing data methods that we deem useful for the analysis of incomplete panel data and discuss, how some of the shortcomings of existing procedures ...... hiện toàn bộ
Information content of partially rank-ordered set samples
AStA Advances in Statistical Analysis - Tập 101 - Trang 117-149 - 2016
Partially rank-ordered set (PROS) sampling is a generalization of ranked set sampling in which rankers are not required to fully rank the sampling units in each set, hence having more flexibility to perform the necessary judgemental ranking process. The PROS sampling has a wide range of applications in different fields ranging from environmental and ecological studies to medical research and it ha...... hiện toàn bộ
A Bayesian approach to modeling topic-metadata relationships
AStA Advances in Statistical Analysis - - Trang 1-17 - 2023
The objective of advanced topic modeling is not only to explore latent topical structures, but also to estimate relationships between the discovered topics and theoretically relevant metadata. Methods used to estimate such relationships must take into account that the topical structure is not directly observed, but instead being estimated itself in an unsupervised fashion, usually by common topic ...... hiện toàn bộ
A model specification test for the variance function in nonparametric regression
AStA Advances in Statistical Analysis - Tập 103 - Trang 387-410 - 2018
The problem of testing for the parametric form of the conditional variance is considered in a fully nonparametric regression model. A test statistic based on a weighted
$$L_2$$
-distance between the empirical characteristic functions of residuals ...... hiện toàn bộ
A semi-parametric quantile regression approach to zero-inflated and incomplete longitudinal outcomes
AStA Advances in Statistical Analysis - Tập 104 - Trang 261-283 - 2020
Quantile regression models are typically used for modeling non-Gaussian outcomes, and such models allow quantile-specific inference. While there exists a vast literature on conditional quantile regression (where the model parameters are estimated precisely for one prefixed quantile level), relatively less work has been reported on joint quantile regression. The challenge in joint quantile regressi...... hiện toàn bộ
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