Abstract: This paper deals with the state estimation of linear time-invariant discrete systems with unknown inputs. The forward sequences of the output are treated as additional outputs. In this case, the rank condition for designing the unknown input estimator is relaxed. The gain for minimal estimation error variance is presented, and a numerical example is given to verify the proposed unknow...

For static panel data models that include endogenous time-invariant variables correlated with individual effects, exogenous averages over time of time-varying can be internal instruments. To pretest their exogeneity, we first estimate a random effects model includes all (Mundlak, 1978; Krishnakumar, 2006). Internal instruments are then selected if parameter is statistically different from zero ...

This paper proposes an asymptotically efficient method for estimating models with conditional moment restrictions. Our estimator generalizes the maximum empirical likelihood estimator (MELE) of Qin and Lawless (1994). Using a kernel smoothing method, we efficiently incorporate the information implied by the conditional moment restrictions into our empirical likelihood-based procedure. This yiel...

We characterize eecient estimators for the expectation of a function under the invariant distribution of a Markov chain and outline ways of constructing such estimators. We consider two models. The rst is described by a parametric family of constraints on the transition distribution; the second is the heteroscedastic nonlinear autoregressive model. The eecient estimator for the rst model adds a...

The log-logistic distribution is widely used in different fields of study such as survival analysis, hydrology, insurance, and economics. Recently, Ahsanullah Alzaatreh studied the best linear unbiased estimators for location scale parameters three-parameter model. same authors also propose a shift-invariant Hill estimator unknown shape parameter. In this work, we new estimation method We deriv...

Long-memory noise is common to many areas of signal processing and can seriously confound estimation of linear regression model parameters and their standard errors. Classical autoregressive moving average (ARMA) methods can adequately address the problem of linear time invariant, short-memory errors but may be inefficient and/or insufficient to secure type 1 error control in the context of fra...

Consider a first order linear time-invariant discrete time system driven by process noise, a preprocessor that accepts causal measurements of the state of the system, and a state estimator. The preprocessor and the state estimator are not co-located, and, at every time-step, the pre-processor transmits either a real number or an erasure symbol to the estimator. We seek the pre-processor and the...