Computing log-likelihood for censored data

Modeling
1

Hi,
I am modelling time series data for a given number of subjects. some data points are censored.
I am currently computing the log-likelihood for each data point in the generated quantities block as a matrix and taking the sum outside Stan.
I am struggling to determine if this is the most efficient way to compute the log-likelihood? Any help would be appreciated.

Thanks

Below is part of my code

data {
       int<lower=1> J;                  //number of subjects
        int<lower=1> n_obs;          //number of observed measurements 
        real<lower =0> y_hat[n_obs,J];  //observed measurements
        int <lower =0, upper =1> is_censored[n_obs,J];  // Indicator matrix: 
       real  L;                                      // censoring point 
       int<lower=0, upper = 1> run_estimation; // a switch to evaluate the likelihood
}
```stan
model {
  vector[N] mu = alpha + beta * x;
  y ~ normal(mu, sigma); 
} 
parameters{
....
}
transformed parameters{
real y_new[n_obs,J];     //mean matrix
}
model{
  if(run_estimation==1){
           for (j in 1:J){
              for (n in 1:n_obs){
                 if (is_censored[n,j] == 0){
                    y_hat[n,j] ~ lognormal(log(y_new[n,j]), std);            //likelihood for uncensored observed data
               } else {
                target += lognormal_lcdf(L |log(y_new[n,j]), std);          //likelihood of censored data
               }
            }
         }
       }
}
 generated quantities {
                   real log_like[n_obs, J]; 

         //compute log likelihood
        if(run_estimation==1){
             for (j in 1:J){
              for (n in 1:n_obs){
                 if (is_censored[n,j] == 0){
                    log_like[n,j] = lognormal_lpdf(y_hat[n,j]|log(y_new[n,j]),std);//likelihood for uncensored observed data
               } else {
                log_like[n,j]= lognormal_lcdf(L |log(y_new[n,j]), std);          //likelihood of censored data
               }
            }
         }
        }
}