Local influence for Liu estimators in semiparametric linear models

Emami, Hadi

doi:10.1007/s00362-016-0775-6

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Rocha and Simas [16] and Ferrari et al [17] derived the normal curvature considering a beta regression model whose dispersion parameter varies according to the effect of some covariates. Ferreira and Paula [18] developed the local influence approach to partially linear Skew Normal models under different perturbation schemes, and Emami [19] evaluated the sensitivity of Liu penalized least squares estimators using local influence analysis. Most recently, Liu et al [20] have reported the implementation of influence diagnostics in AR time series models with Skew Normal (SK) distributions.…”

Section: Introductionmentioning

confidence: 99%

Local Influence for the Thin-Plate Spline Generalized Linear Model

Ibacache-Pulgar,

Pacheco,

Nicolis

et al. 2024

Axioms

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Thin-Plate Spline Generalized Linear Models (TPS-GLMs) are an extension of Semiparametric Generalized Linear Models (SGLMs), because they allow a smoothing spline to be extended to two or more dimensions. This class of models allows modeling a set of data in which it is desired to incorporate the non-linear joint effects of some covariates to explain the variability of a certain variable of interest. In the spatial context, these models are quite useful, since they allow the effects of locations to be included, both in trend and dispersion, using a smooth surface. In this work, we extend the local influence technique for the TPS-GLM model in order to evaluate the sensitivity of the maximum penalized likelihood estimators against small perturbations in the model and data. We fit our model through a joint iterative process based on Fisher Scoring and weighted backfitting algorithms. In addition, we obtained the normal curvature for the case-weight perturbation and response variable additive perturbation schemes, in order to detect influential observations on the model fit. Finally, two data sets from different areas (agronomy and environment) were used to illustrate the methodology proposed here.

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Section: Introductionmentioning

confidence: 99%

Local Influence for the Thin-Plate Spline Generalized Linear Model

Ibacache-Pulgar,

Pacheco,

Nicolis

et al. 2024

Axioms

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“…Numerous disciplines, including economics and business, have paid close attention to the semi-parametric regression model because of its efficacy in extracting insights from data, and because of the versatility with which they can mimic occurrences [1,2]. For example, take the model: 𝑦 𝑗 = 𝑥 𝑗 𝛼 + 𝑔(𝑡 𝑗 ) + 𝜀 𝑗, 𝑗 = 1,2, .…”

Section: Introductionmentioning

confidence: 99%

“…. , 𝑥 𝑖𝑓 ) is a vector of explanatory variables, the 𝑡 𝑗 's are known and non-random in some bounded domain 𝐷 ⊂ ℝ, 𝑔(𝑡 𝑗 ) is a vector of unknown smooth functions, and 𝜀 𝑗 's are independent and identically distributed random errors with mean 0 and variance 𝜎 2 that are unrelated to (𝑥 𝑗 , 𝑡 𝑗 ) [1]. Many semiparametric regression methods borrow ideas from other, more generalized methods of estimating regression parameters.…”

Section: Introductionmentioning

confidence: 99%

Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

Algamal

AL-Taie

Qasim

2023

ijcsm

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Statistical modeling is essential in many scientific research areas because it explains the relationship between the response variable of interest and a number of explanatory variables. However, it is not easy to determine the optimal model beforehand. Therefore, in this paper, we look at how to choose a hyper-parameter in a kernel semi-parametric regression model. A quasi-oppositional learning pelican optimization algorithm strategy is used to select the smoothness parameter. In comparison to other competitor approaches, simulation results revealed that the suggested method, the quasi-oppositional learning pelican optimization algorithm, is superior in terms of MSE. The experimental findings and statistical analysis show that when compared to the CV and GCV, our proposed quasi-oppositional learning pelican optimization algorithm provides greater performance in terms of computational time.

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“…There does not seem to have any work on diagnostics for semiparametric regression models in present of collinearity. Recently, Emami (2015) and Emami (2016) developed influence diagnostics based case deletion and local influence approach for ridge estima-tors and Liu estimators in semiparametric regression models, respectively. In this paper, therefore, we propose a case deletion formula to detect influential points in LE for semiparametric models.…”

Section: Introductionmentioning

confidence: 99%

Case Deletion Diagnostics in Liu Semiparametric Regression Models

Emami¹

2021

Journal of Data Science

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In semiparametric regression it is of interest to detect anomalous observations that exert an unduly large influence on the parameter's esti-mate and fitted values. Usually the existence of influential observations is complicated by the presence of collinearity. However no method of influ-ence diagnostics available for the possible effects that collinearity can have on the influence of an observation on the estimates of parametric and non-parametric component of semiparametric regression models. In this paper we show when Liu estimators are used to mitigate the effects of collinearity the influence of some observations can be drastically modified. We propose a case deletion formula to detect influential points in Liu estimators of semi-parametric regression models . As an illustrative example a real data set are analysed.

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Local influence for Liu estimators in semiparametric linear models

Cited by 8 publications

References 30 publications

Local Influence for the Thin-Plate Spline Generalized Linear Model

Local Influence for the Thin-Plate Spline Generalized Linear Model

Kernel semi-parametric model improvement based on quasi-oppositional learning pelican optimization algorithm

Case Deletion Diagnostics in Liu Semiparametric Regression Models

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