Monte Carlo Method Assuming Wishart Distribution using Estimated Covariance Matrix for Data Generated from a Multivariate Normal Distribution

Monte Carlo Method Assuming Wishart Distribution using Estimated Covariance Matrix for Data Generated from a Multivariate Normal Distribution

mvn_std_mc.wishart(data, R = 20000L, taskid)

Arguments

data	n by 3 matrix or data frame. data[, 1] correspond to values for x. data[, 2] correspond to values for m. data[, 3] correspond to values for y.
R	Integer. Monte Carlo replications.
taskid	Numeric. Task ID.

See also

Other monte carlo method functions: beta_ols_mc.mvn_pcci_simulation(), beta_ols_mc.mvn_pcci_task(), beta_ols_mc.mvn_simulation(), beta_ols_mc.mvn_task(), beta_ols_mc.mvn(), exp_ols_mc.mvn_pcci_simulation(), exp_ols_mc.mvn_pcci_task(), exp_ols_mc.mvn_simulation(), exp_ols_mc.mvn_task(), exp_ols_mc.mvn(), mc.mvn(), mc.t(), mc.wishart(), mvn_mar_10_mc.mvn_pcci_simulation(), mvn_mar_10_mc.mvn_pcci_task(), mvn_mar_10_mc.mvn_simulation(), mvn_mar_10_mc.mvn_task(), mvn_mar_10_mc.mvn(), mvn_mar_20_mc.mvn_pcci_simulation(), mvn_mar_20_mc.mvn_pcci_task(), mvn_mar_20_mc.mvn_simulation(), mvn_mar_20_mc.mvn_task(), mvn_mar_20_mc.mvn(), mvn_mar_30_mc.mvn_pcci_simulation(), mvn_mar_30_mc.mvn_pcci_task(), mvn_mar_30_mc.mvn_simulation(), mvn_mar_30_mc.mvn_task(), mvn_mar_30_mc.mvn(), mvn_mcar_10_mc.mvn_pcci_simulation(), mvn_mcar_10_mc.mvn_pcci_task(), mvn_mcar_10_mc.mvn_simulation(), mvn_mcar_10_mc.mvn_task(), mvn_mcar_10_mc.mvn(), mvn_mcar_20_mc.mvn_pcci_simulation(), mvn_mcar_20_mc.mvn_pcci_task(), mvn_mcar_20_mc.mvn_simulation(), mvn_mcar_20_mc.mvn_task(), mvn_mcar_20_mc.mvn(), mvn_mcar_30_mc.mvn_pcci_simulation(), mvn_mcar_30_mc.mvn_pcci_task(), mvn_mcar_30_mc.mvn_simulation(), mvn_mcar_30_mc.mvn_task(), mvn_mcar_30_mc.mvn(), mvn_mnar_10_mc.mvn_pcci_simulation(), mvn_mnar_10_mc.mvn_pcci_task(), mvn_mnar_10_mc.mvn_simulation(), mvn_mnar_10_mc.mvn_task(), mvn_mnar_10_mc.mvn(), mvn_mnar_20_mc.mvn_pcci_simulation(), mvn_mnar_20_mc.mvn_pcci_task(), mvn_mnar_20_mc.mvn_simulation(), mvn_mnar_20_mc.mvn_task(), mvn_mnar_20_mc.mvn(), mvn_mnar_30_mc.mvn_pcci_simulation(), mvn_mnar_30_mc.mvn_pcci_task(), mvn_mnar_30_mc.mvn_simulation(), mvn_mnar_30_mc.mvn_task(), mvn_mnar_30_mc.mvn(), mvn_ols_mc.mvn_pcci_simulation(), mvn_ols_mc.mvn_pcci_task(), mvn_ols_mc.mvn_simulation(), mvn_ols_mc.mvn_task(), mvn_ols_mc.mvn(), mvn_sem_mc.mvn_pcci_simulation(), mvn_sem_mc.mvn_pcci_task(), mvn_sem_mc.mvn_simulation(), mvn_sem_mc.mvn_task(), mvn_sem_mc.mvn(), mvn_std_mc.mvn.delta_pcci_simulation(), mvn_std_mc.mvn.delta_pcci_task(), mvn_std_mc.mvn.delta_simulation(), mvn_std_mc.mvn.delta_task(), mvn_std_mc.mvn.delta(), mvn_std_mc.mvn.sem_pcci_simulation(), mvn_std_mc.mvn.sem_pcci_task(), mvn_std_mc.mvn.sem_simulation(), mvn_std_mc.mvn.sem_task(), mvn_std_mc.mvn.sem(), mvn_std_mc.mvn.tb_pcci_simulation(), mvn_std_mc.mvn.tb_pcci_task(), mvn_std_mc.mvn.tb_simulation(), mvn_std_mc.mvn.tb_task(), mvn_std_mc.mvn.tb(), mvn_std_mc.wishart_pcci_simulation(), mvn_std_mc.wishart_pcci_task(), mvn_std_mc.wishart_simulation(), mvn_std_mc.wishart_task(), vm_mod_ols_mc.mvn_pcci_simulation(), vm_mod_ols_mc.mvn_pcci_task(), vm_mod_ols_mc.mvn_simulation(), vm_mod_ols_mc.mvn_task(), vm_mod_ols_mc.mvn(), vm_mod_sem_mc.mvn_pcci_simulation(), vm_mod_sem_mc.mvn_pcci_task(), vm_mod_sem_mc.mvn_simulation(), vm_mod_sem_mc.mvn_task(), vm_mod_sem_mc.mvn(), vm_sev_ols_mc.mvn_pcci_simulation(), vm_sev_ols_mc.mvn_pcci_task(), vm_sev_ols_mc.mvn_simulation(), vm_sev_ols_mc.mvn_task(), vm_sev_ols_mc.mvn(), vm_sev_sem_mc.mvn_pcci_simulation(), vm_sev_sem_mc.mvn_pcci_task(), vm_sev_sem_mc.mvn_simulation(), vm_sev_sem_mc.mvn_task(), vm_sev_sem_mc.mvn()

Author

Ivan Jacob Agaloos Pesigan

Examples

taskid <- 1
data <- mvn_dat(taskid = taskid)
fit.ols(data = data, minimal = TRUE, std = TRUE)
#> [1] 0.4998173

thetahatstar <- mvn_std_mc.wishart(
  data = data, R = 20000L, taskid = taskid
)
hist(thetahatstar)