Bayesian Analysis for a Functional Regression Model with Truncated Errors in Variables |
Kim, Hea-Jung (Department of Statistics, Dongguk University) |
1 |
Variable selection and model comparison in regression
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2 |
Markov chain Monte Carlo convergence diagnostics: A comparative review
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DOI ScienceOn |
3 |
Using the singly truncated normal distribution to analyze satellite data
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DOI |
4 |
Sampling-based approaches to calculating marginal densities
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DOI ScienceOn |
5 |
Generalized least squares with ignored errors in variables
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DOI ScienceOn |
6 |
A probabilistic representation of the skewed-normal distribution
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7 |
Exact likelihood analysis of the multinomial probit model
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DOI ScienceOn |
8 |
Bayesian analysis of multivariate probit models in MCMC
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9 |
Bayesian analysis of binary and polytomous response data
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DOI ScienceOn |
10 |
Errors of measurement in Statistics
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11 |
Simulation-based estimation
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12 |
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13 |
Least squares and grouping method estimators in the errors in variables model
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DOI ScienceOn |
14 |
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15 |
A method of simulated moments for estimation of discrete response models without numerical integration
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DOI ScienceOn |
16 |
On the moment of positively truncated normal distribution
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17 |
The multivariate skew-normal distribution
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DOI ScienceOn |
18 |
Some effects of ignoring correlated measurement errors in straight line regression
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DOI ScienceOn |
19 |
Bayesian modeling of correlated binary response responses via scale mixture of multivariate normal link model functions
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