In nonlinear regression theory, the sandwich estimator of the covariance matrix of the model parameters is known as a consistent estimator, even when the parameterized model does not contain the regression. However, in the latter case, we emphasize the fact that the consistency of the sandwich holds only if the inputs of the training set are the values of independent identically distributed ran...

A nonlinear regression model with correlated, normally distributed stationary errors is investigated. Limit properties of an approximate estimator of an unknown covariance function of stationary errors are studied and suucient conditions under which this estimator is consistent are shown.

In this paper, we consider the problem of estimating the covariance kernel and its eigenvalues and eigenfunctions from sparse, irregularly observed, noise corrupted and (possibly) correlated functional data. We present a method based on pre-smoothing of individual sample curves through an appropriate kernel. We show that the naive empirical covariance of the pre-smoothed sample curves gives hig...

This paper proposes a new instrumental-type estimator of quantile regression models for panel data with fixed effects. The is built upon the minimum distance, which defined as weighted average conventional individual instrumental variable slope estimators. weights assigned to each are determined by inverses their corresponding variance–covariance matrices. implementation estimation has many adv...

In this paper, we establish asymptotic normality of Powell’s kernel estimator for the asymptotic covariance matrix of the quantile regression estimator for both i.i.d. and weakly dependent data. As an application, we derive the optimal bandwidth that minimizes the approximate mean squared error of the kernel estimator.

We introduce nonparametric regularization of the eigenvalues of a sample covariance matrix through splitting of the data (NERCOME), and prove that NERCOME enjoys asymptotic optimal nonlinear shrinkage of eigenvalues with respect to the Frobenius norm. One advantage of NERCOME is its computational speed when the dimension is not too large. We prove that NERCOME is positive definite almost surely...

We introduce nonparametric regularization of the eigenvalues of a sample covariance matrix through splitting of the data (NERCOME), and prove that NERCOME enjoys asymptotic optimal nonlinear shrinkage of eigenvalues with respect to the Frobenius norm. One advantage of NERCOME is its computational speed when the dimension is not too large. We prove that NERCOME is positive definite almost surely...

Targeted minimum loss based estimation (TMLE) provides a template for the construction of semiparametric locally efficient double robust substitution estimators of the target parameter of the data generating distribution in a semiparametric censored data or causal inference model (van der Laan and Rubin (2006), van der Laan (2008), van der Laan and Rose (2011)). In this article we demonstrate ...

Previous investigations have shown that the canonical approach to simulating QCD at finite density is promising. The algorithm we used in our earlier work employs an exact calculation of the fermionic determinant which limits the size of the lattices we can simulate. Interesting questions can only be answered if we simulate at larger volume. In this paper we explore an algorithm, Hybrid Noisy M...

Often we build parameter estimators for which it is difficult compute the expectation and covariance analytically. Instead, we can estimate the expected value and the covariance through Monte Carlo Integration. This procedure estimates the expectation and covariance by applying the estimator to synthetic data polluted by random noise.