On the strong uniform consistency of the mode estimator for censored time series

Khardani, Salah; Lemdani, Mohamed; Saïd, Elias Ould

doi:10.1007/s00184-010-0324-6

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…There have been several important studies about right-censored time-series models, including Park et al [12], Khardani et al [13], Aydın and Yılmaz [14]. These studies do not consider nonlinearity and instead focus on different aspects of modeling.…”

Section: Introductionmentioning

confidence: 99%

“…These studies do not consider nonlinearity and instead focus on different aspects of modeling. However, useful solution techniques are introduced for the right-censorship problem, such as synthetic data kNN imputation, Kaplan-Meier weights (see [14]), and synthetic data transformation [15], which is adapted to the time series modeling by [13].…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Right-censored SETAR-type Nonlinear Time-series Model

2023

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This paper focuses on estimating the Self-Exciting Threshold Autoregressive (SETAR) type time-series model under right-censored data. As is known, the SETAR model is used when the underlying function of the relation-ship between the time-series itself (Yt), and its p delays $$({Y_{t - j}})_{j = 1}^p$$ violates the lin-earity assumption and this function is formed by multiple behaviors that called regime. This paper addresses the right-censored dependent time-series problem which has a serious negative effect on the estimation performance. Right-censored time series cause biased coefficient estimates and unqualified predictions. The main contribution of this paper is solving the censorship problem for the SETAR by three different techniques that are kNN imputation which represents the imputation techniques, Kaplan-Meier weights that is applied based on the weighted least squares, synthetic data transformation which adds the effect of censorship to the modeling process by manipulating dataset. Then, these solutions are combined by the SETAR-type model estimation process. To observe the behavior of the nonlinear estimators in practice, a simulation study and a real data example are carried out. The Covid-19 dataset collected in China is used as real data. Results prove that although the three estimators show satisfying performance, the quality of the estimate SETAR model based on the kNN imputation technique dominates the other two estimators.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Right-censored SETAR-type Nonlinear Time-series Model

2023

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“…Other authors have been interested in the estimation of conditional models from censored or truncated observations (see, e.g. [12][13][14][15][16][17][18]). Many statistical applications had to involve a variable of duration denoted , designating the time elapsed until the occurrence of the event of interest.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic Normality Single Functional Index Quantile Regression Under Randomly Censored Data

2022

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The main objective of this paper is to estimate non-parametrically the quantiles of a conditional distribution based on the single-index model in the censorship model when the sample is considered as an independent and identically distributed (i.i.d.) random variables. First of all, a kernel type estimator for the conditional cumulative distribution function (cond-cdf) is introduced. Afterwards, we give an estimation of the quantiles by inverting this estimated cond-cdf, the asymptotic properties are stated when the observations are linked with a single-index structure. Finally, a simulation study is carried out to evaluate the performance of this estimate.

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