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Georgiev, Alexander A. - In: Statistics & Probability Letters 10 (1990) 3, pp. 203-211

Consider the d-dimensional unit cube [0,1]d and portion it into n regions, A1,..., An. Select and fix a point in each one of these regions so we have x1,..., xn. Consider observable variables Yi, i = 1,..., n, satisfying the multivariate regression model Yi = g(xi) + [var epsilon]i, where g is...

Persistent link: https://www.econbiz.de/10005254143

A smoothing stochastic algorithm for quantile estimation

Amiri, Aboubacar; Thiam, Baba - In: Statistics & Probability Letters 93 (2014) C, pp. 116-125

In this paper, we provide the almost-sure convergence and the asymptotic normality of a smooth version of the Robbins–Monro algorithm for the quantile estimation. A Monte Carlo simulation study shows that our proposed method works well within the framework of a data stream.

Persistent link: https://www.econbiz.de/10010906229

Estimating with kernel smoothers the mean of functional data in a finite population setting. A note on variance estimation in presence of partially observed trajectories

Cardot, Hervé; De Moliner, Anne; Goga, Camelia - In: Statistics & Probability Letters 99 (2015) C, pp. 156-166

This paper studies, in a survey sampling framework with unequal probability sampling designs, three nonparametric kernel estimators for the mean curve in presence of discretized trajectories with missing values. Their pointwise variances are approximated thanks to linearization techniques.

Persistent link: https://www.econbiz.de/10011208308

Identification for semiparametric varying coefficient partially linear models

Wang, Mingqiu; Song, Lixin - In: Statistics & Probability Letters 83 (2013) 5, pp. 1311-1320

In this paper, we apply the group smoothly clipped absolute deviation (SCAD) penalty to identify the model structure of the semiparametric varying coefficient partially linear model. The performance of the new approach is demonstrated in terms of the theoretical and numerical results.

Persistent link: https://www.econbiz.de/10010662322

Bayesian model selection based on parameter estimates from subsamples

Zhang, Jingsi; Jiang, Wenxin; Shao, Xiaofeng - In: Statistics & Probability Letters 83 (2013) 4, pp. 979-986

We propose Bayesian model selection based on composite datasets, which can be constructed from various subsample estimates. The method remains consistent without fully specifying a probability model, and is useful for dependent data, when asymptotic variance of the parameter estimator is...

Persistent link: https://www.econbiz.de/10010662324

Nearly universal consistency of maximum likelihood in discrete models

Seo, Byungtae; Lindsay, Bruce G. - In: Statistics & Probability Letters 83 (2013) 7, pp. 1699-1702

The consistency of the maximum likelihood estimator (MLE) has been well studied in many papers such as Wald (1949 … understand the consistency of the MLE in discrete models. In addition, our work gives a very general and direct proof for the … consistency of the MLE by introducing a parameter-free version of consistency. …

Persistent link: https://www.econbiz.de/10010665597

Estimation in a change-point hazard regression model with long-term survivors

Li, Yunxia; Qian, Lianfen; Zhang, Wei - In: Statistics & Probability Letters 83 (2013) 7, pp. 1683-1691

This paper estimates the change-point for a piecewise hazard regression model in the presence of right censoring and long-term survivors. The maximum likelihood estimators of the change point and other parameters are shown to be consistent. The proposed method is illustrated through analyzing...

Persistent link: https://www.econbiz.de/10010665603

Consistency results for the kernel density estimate on continuous time stationary and dependent data

Didi, Sultana; Louani, Djamal - In: Statistics & Probability Letters 83 (2013) 4, pp. 1262-1270

The aim of this paper is to study the consistency of the kernel density estimator pertaining to a continuous time …

Persistent link: https://www.econbiz.de/10011039913

Is the p-value a good measure of evidence? Asymptotic consistency criteria

Grendár, M. - In: Statistics & Probability Letters 82 (2012) 6, pp. 1116-1119

should be consistent. Consistency is an asymptotic criterion: the probability that, if a measure of evidence in data strongly … consistent, while the ratio of likelihoods is. The same holds also with respect to the unconditional consistency criterion. …

Persistent link: https://www.econbiz.de/10011039952

Estimating an endpoint with high order moments in the Weibull domain of attraction

Girard, Stéphane; Guillou, Armelle; Stupfler, Gilles - In: Statistics & Probability Letters 82 (2012) 12, pp. 2136-2144

give conditions on the rate of divergence to get the weak and strong consistency as well as the asymptotic normality of the …

Persistent link: https://www.econbiz.de/10010580426