We propose an empirical likelihood-based estimation method for conditional estimating equations containing unknown functions, which can be applied for various semiparametric models. The proposed method is based on the methods of conditional empirical likelihood and penalization. Thus, our...

We consider the problem of estimating quantile regression coefficients in errors-in-variables models. When the error variables for both the response and the manifest variables have a joint distribution that is spherically symmetric but otherwise unknown, the regression quantile estimates based...

We consider the problem of estimating quantile regression coefficients in errors-in-variables models. When the error variables for both the response and the manifest variables have a joint distribution that is spherically symmetric but otherwise unknown, the regression quantile estimates based...

This paper complements the results of Hausman and Taylor (1981) and Cornwell, Schmidt and Sickles (1990) and generalizes Park and Simar (1994) by examining the semiparametric efficient estimation of panel models in which the random effects and the regressors have certain patterns of correlation....

We consider the inverse problem of estimating a survival distribution when the survival times are only observed to be in one of the intervals of a random bisection of the time axis. We are particularly interested in the case that high-dimensional and/or time-dependent covariates are available,...

Existing specification tests for conditional heteroskedasticity are derived under the assumption that the density of the innovation, or standardized error, is Gaussian, despite the fact that many recent empirical studies provide evidence that this density is not Gaussian. We obtain specification...