The article discusses how to estimate parameters for nonlinear Wiener processes with measurement errors. The researchers show that using the first differences of observations can simplify the estimation process. They also introduce a method to calculate the covariance matrix more accurately. The assumption of unit-specific effects is crucial for accurate estimation. A modified algorithm is proposed for parameter estimation with random effects. The researchers demonstrate the effectiveness of their method through numerical examples and comparisons with existing techniques.