Models with multiple change points are used in many fields; however, the theoretical properties of maximum likelihood estimators of such models have received relatively little attention. The goal of this paper is to establish the asymptotic properties of maximum likelihood estimators of the parameters of a multiple-change-point model for a general class of models in which the form of the distri...

This paper presents a speech-model using the Linear Predictive (LP) residual signal and Maximum Likelihood Estimator (MLE). With this model an accuracy of the reverberation time estimation can be improved. During past decade, the reverberation time estimation was performed using only maximum likelihood detector, which resulted in excess time of estimation. For the purpose of estimating room aco...

The EGARCH is a popular model for discrete time volatility since it allows for asymmetric effects and naturally ensures positivity even when including exogenous variables. Estimation and inference is usually done via maximum likelihood. Although some progress has been made recently, a complete distribution theory of MLE for EGARCH models is still missing. Furthermore, the estimation procedure i...

We establish global rates of convergence of the Maximum Likelihood Estimator (MLE) of a multivariate distribution function on ℝ d in the case of (one type of) "interval censored" data. The main finding is that the rate of convergence of the MLE in the Hellinger metric is no worse than n-1/3(log n)γ for γ = (5d - 4)/6.

Interpretable, Computationally Tractable Approximate Parameter Estimation for Corporate Defaults

In this paper we consider the problem of testing (a) sphericity and (b) intraclass covariance structure under a Growth Curve model. The maximum likelihood estimator (MLE) for the mean in a Growth Curve model is a weighted estimator with the inverse of the sample covariance matrix which is unstable for large p close to N and singular for p larger than N . The MLE for the covariance matrix is bas...

• In this paper, we study the asymptotic properties of the maximum likelihood estimator (MLE) in COGARCH(1,1) models driven by Lévy processes as proposed by Maller et al. ([13]). We show that the MLE is consistent and asymptotically normal under some conditions relevant to the moments of the driving Lévy process and the sampling scheme.

In this paper a software reliability growth model (SRGM) based on non-homogenous Poisson process (NHPP) is proposed. The principal idea is to provide a SRGM which incorporates both exponentiated Weibull (EW) testing-effort function and change point. In the earlier research, it is found that the probability of fault detection is not constant. It can be changed at some point of time which is call...

We study statistical estimators computed using iterative optimization methods that are not run until completion. Classical results on maximum likelihood (MLEs) assert a one-step estimator (OSE), in which single Newton-Raphson iteration is performed from starting point with certain properties, asymptotically equivalent to the MLE. further develop these early-stopping by deriving properties of de...

The paper studies large sample asymptotic properties of the Maximum Likelihood Estimator (MLE) for the parameter of a continuous time Markov chain, observed in white noise. Using the method of weak convergence of likelihoods due to I.Ibragimov and R.Khasminskii [14], consistency, asymptotic normality and convergence of moments are established for MLE under certain strong ergodicity conditions o...