Coverage

calculate 95% coverage for varying effects and constant effects under example data

Our recently developed fully Bayesian semiparametric quantile mixed-effect model for high-dimensional longitudinal studies with heterogeneous observations can be implemented through this package. This model can distinguish between time-varying interactions and constant-effect-only cases to avoid model misspecifications. Facilitated by spike-and-slab priors, this model leads to superior performance in estimation, identification and statistical inference. In particular, robust Bayesian inferences in terms of valid Bayesian credible intervals on both parametric and nonparametric effects can be validated on finite samples. The Markov chain Monte Carlo algorithms of the proposed and alternative models are efficiently implemented in 'C++'.

Kun Fan

Blend

Bayesian Longitudinal Regularized Semiparametric Quantile Mixed
Models

Cen Wu

Coverage function

<dl><dt>x</dt>
<dd>Blend object.</dd></dl>

Arguments

95% coverage for a Blend object with structural identification — Coverage

<dl>

<dt>x</dt>
<dd>Blend object.</dd>

</dl>

Coverage: 95% coverage for a Blend object with structural identification

Description

Usage

Value

Arguments

See Also

Examples