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    Eliminating bias due to censoring in Kendall’s tau estimators for quasi independence of truncation and failure 

    Type: Journal Paper
    Author : Austin, Matthew D. - Betensky, Rebecca A.
    Publisher: Elsevier Science
    Year: 2014

    Asymptotic behaviors of nearest neighbor kernel density estimator in left-truncated data 

    Type: Journal Paper
    Author : راحله زمینی; وحید فکور; مجید سرمد; raheleh zamini; Vahid Fakoor; Majid Sarmad
    Year: 2014
    Abstract:

    depending on the location of the sample points. In this paper‎, we initially introduce the k-nearest neighbor kernel density estimator in the random left-truncation model, and then prove some of its asymptotic behaviors...

    Uniform limit theorems under random truncation 

    Type: Conference Paper
    Author : وحید فکور; دکتر راحله زمینی; Vahid Fakoor
    Year: 2016
    Abstract:

    In this pap er we study uniform versions of the two limit theorems in random left

    uncation mo del (RLTM). The law of large numb ers (LLN) and the central limit theorem

    CLT) have b een obtained under the ...

    A Berry-Esseen Type Bound in Kernel Density Estimation for a Random Left-Truncation Model 

    Type: Journal Paper
    Author : پطروس اصغری; وحید فکور; مجید سرمد; Petros Asghari; Vahid Fakoor; Majid Sarmad
    Year: 2014
    Abstract:

    In this paper we derive a Berry-Esseen type bound for the kernel density estimator of a random left truncated model, in which each datum ( Y ) is randomly left truncated and it is sampled if Y ≥ T , where T is the truncation random variable...

    A Berry-Esseen type bound for the kernel density estimator based on a weakly dependent and randomly left truncated data 

    Type: Journal Paper
    Author : پطروس اصغری; وحید فکور; Petros Asghari; Vahid Fakoor
    Year: 2017
    Abstract:

    In many applications, the available data come from a sampling scheme that causes

    loss of information in terms of left truncation. In some cases, in addition to left

    truncation, the data are weakly dependent. In this paper we...

    Weak convergence of TJW product-limit estimator under association 

    Type: Journal Paper
    Author : امیرحسین شبانی; هادی جباری نوقابی; وحید فکور; Amir hossein Shabani; Hadi Jabbari Nooghabi; Vahid Fakoor
    Year: 2019
    Abstract:

    Estimation of a survival function and its asymptotic behaviors under random left truncation and right censor model had been extensively studied in the five past decades. In application, the assumption of independence between cases is often not true...

    Asymptotic normality of the truncation probability estimator for truncated dependent data 

    Type: Journal Paper
    Author : سارا جمهوری شوکت آباد; وحید فکور; حسنعلی آذرنوش; Sara Jomhoori; Vahid Fakoor; Hassan Ali Azarnoosh
    Year: 2009
    Abstract:

    In some long term studies, a series of dependent and possibly

    truncated lifetimes may be observed. Suppose that the lifetimes

    have a common marginal distribution function. In the

    left-truncation model, one observes data (X...

    Estimation with left-truncated and right censored data: A comparison study 

    Type: Journal Paper
    Author : مهدی دوست پرست; جعفر احمدی; Ahmad‎ ‎Parsian; Mahdi Doostparast; Jafar Ahmadi
    Year: 2012
    Abstract:

    ‎Estimation based on the left-truncated and right randomly censored data arising from a general family of distributions is considered‎. ‎In the‎

    ‎special case‎, ‎when the random variables satisfy a proportional hazard model‎, ‎the maximum...

    Strong Convergence Rates of the Product-limit Estimator for Left Truncated and Right Censored Data under Association 

    Type: Journal Paper
    Author : امیرحسین شبانی; هادی جباری نوقابی; وحید فکور; Amir hossein Shabani; Hadi Jabbari Nooghabi; Vahid Fakoor
    Year: 2019
    Abstract:

    Non-parametric estimation of a survival function from left truncated data subject to right censoring has been extensively studied in the literature. It is commonly assumed in such studies that the lifetime variables are a sample of independent...

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