Relevant bibliographies by topics / Semi-parametric single index

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  1. Journal articles
  2. Dissertations / Theses
  3. Conference papers

Academic literature on the topic 'Semi-parametric single index'

Author: Grafiati
Published: 1 March 2025

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Journal articles on the topic "Semi-parametric single index"

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Masten, Arjana Brezigar, and Igor Masten. "Predicting Bankruptcy with Semi-Parametric Single-Index Model." Economic Research-Ekonomska Istraživanja 25, no. 1 (January 2012): 99–108. http://dx.doi.org/10.1080/1331677x.2012.11517497.

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Zhou, Xiao-Hua, and Hua Liang. "Semi-parametric single-index two-part regression models." Computational Statistics & Data Analysis 50, no. 5 (March 2006): 1378–90. http://dx.doi.org/10.1016/j.csda.2004.12.001.

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Xia, Yingcun, and Wolfgang Härdle. "Semi-parametric estimation of partially linear single-index models." Journal of Multivariate Analysis 97, no. 5 (May 2006): 1162–84. http://dx.doi.org/10.1016/j.jmva.2005.11.005.

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Zhou, Weilun, Jiti Gao, David Harris, and Hsein Kew. "Semi-parametric single-index predictive regression models with cointegrated regressors." Journal of Econometrics 238, no. 1 (January 2024): 105577. http://dx.doi.org/10.1016/j.jeconom.2023.105577.

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Birke, Melanie, Sebastien Van Bellegem, and Ingrid Van Keilegom. "Semi-parametric Estimation in a Single-index Model with Endogenous Variables." Scandinavian Journal of Statistics 44, no. 1 (October 20, 2016): 168–91. http://dx.doi.org/10.1111/sjos.12247.

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Ahmed, Huda Yahya, and Munaf Yousif Hmood. "Comparison of Some Semi-parametric Methods in Partial Linear Single-Index Model." Journal of Economics and Administrative Sciences 27, no. 130 (December 1, 2021): 170–84. http://dx.doi.org/10.33095/jeas.v27i130.2207.

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The research dealt with a comparative study between some semi-parametric estimation methods to the Partial linear Single Index Model using simulation. There are two approaches to model estimation two-stage procedure and MADE to estimate this model. Simulations were used to study the finite sample performance of estimating methods based on different Single Index models, error variances, and different sample sizes , and the mean average squared errors were used as a comparison criterion between the methods were used. The results showed a preference for the two-stage procedure depending on all the cases that were used
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Alahiane, Mohamed, Idir Ouassou, Mustapha Rachdi, and Philippe Vieu. "High-Dimensional Statistics: Non-Parametric Generalized Functional Partially Linear Single-Index Model." Mathematics 10, no. 15 (July 30, 2022): 2704. http://dx.doi.org/10.3390/math10152704.

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We study the non-parametric estimation of partially linear generalized single-index functional models, where the systematic component of the model has a flexible functional semi-parametric form with a general link function. We suggest an efficient and practical approach to estimate (I) the single-index link function, (II) the single-index coefficients as well as (III) the non-parametric functional component of the model. The estimation procedure is developed by applying quasi-likelihood, polynomial splines and kernel smoothings. We then derive the asymptotic properties, with rates, of the estimators of each component of the model. Their asymptotic normality is also established. By making use of the splines approximation and the Fisher scoring algorithm, we show that our approach has numerical advantages in terms of the practical efficiency and the computational stability. A computational study on data is provided to illustrate the good practical behavior of our methodology.
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Kadiri, Nadia, Sanaà Dounya Mekki, and Abbes Rabhi. "Single Functional Index Quantile Regression for Functional Data with Missing Data at Random." Econometrics 27, no. 3 (2023): 1–19. http://dx.doi.org/10.15611/eada.2023.3.01.

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The primary goal of this research was to estimate the quantile of a conditional distribution using a semi-parametric approach in the presence of randomly missing data, where the predictor variable belongs to a semi-metric space. The authors assumed a single index structure to link the explanatory and response variable. First, a kernel estimator was proposed for the conditional distribution function, assuming that the data were selected from a stationary process with missing data at random (MAR). By imposing certain general conditions, the study established the model’s uniform almost complete consistencies with convergence rates.
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Feng, Guohua, Bin Peng, Liangjun Su, and Thomas Tao Yang. "Semi-parametric single-index panel data models with interactive fixed effects: Theory and practice." Journal of Econometrics 212, no. 2 (October 2019): 607–22. http://dx.doi.org/10.1016/j.jeconom.2019.05.018.

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Chang, Chaojie. "Research on Two-stage Estimation of Partially Linear Single-index Model with Longitudinal Data." Academic Journal of Science and Technology 5, no. 1 (February 28, 2023): 112–15. http://dx.doi.org/10.54097/ajst.v5i1.5438.

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Partial linear single-index model is a kind of semi-parametric model with wide application. In this paper, we deal with the partial linear single-index model under longitudinal data. A "two-stage estimation method" without iteration by using local polynomial and bias correction generalized estimation equation is proposed. under some regularity conditions, the asymptotic properties of the connection function and unknown parameter estimator are investigated. Numerical simulation shows that the proposed method is robust.
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Dissertations / Theses on the topic "Semi-parametric single index"

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Song, Rui, Shikai Luo, Donglin Zeng, Hao Helen Zhang, Wenbin Lu, and Zhiguo Li. "Semiparametric single-index model for estimating optimal individualized treatment strategy." INST MATHEMATICAL STATISTICS, 2017. http://hdl.handle.net/10150/625783.

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Different from the standard treatment discovery framework which is used for finding single treatments for a homogenous group of patients, personalized medicine involves finding therapies that are tailored to each individual in a heterogeneous group. In this paper, we propose a new semiparametric additive single-index model for estimating individualized treatment strategy. The model assumes a flexible and nonparametric link function for the interaction between treatment and predictive covariates. We estimate the rule via monotone B-splines and establish the asymptotic properties of the estimators. Both simulations and an real data application demonstrate that the proposed method has a competitive performance.
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Xu, Yangyi. "Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/71285.

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We provide a Frequentist-Bayesian hybrid test statistic in this dissertation for two testing problems. The first one is to design a test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the first testing problem, we consider the statistical difference among massive outcomes or signals to be of interest in many diverse fields including neurophysiology, imaging, engineering, and other related fields. However, such data often have nonlinear system, including to row/column patterns, having non-normal distribution, and other hard-to-identifying internal relationship, which lead to difficulties in testing the significance in difference between them for both unknown relationship and high-dimensionality. In this dissertation, we propose an Adaptive Bayes Sum Test capable of testing the significance between two nonlinear system basing on universal non-parametric mathematical decomposition/smoothing components. Our approach is developed from adapting the Bayes sum test statistic by Hart (2009). Any internal pattern is treated through Fourier transformation. Resampling techniques are applied to construct the empirical distribution of test statistic to reduce the effect of non-normal distribution. A simulation study suggests our approach performs better than the alternative method, the Adaptive Neyman Test by Fan and Lin (1998). The usefulness of our approach is demonstrated with an application in the identification of electronic chips as well as an application to test the change of pattern of precipitations. For the second testing problem, currently numerous statistical methods have been developed for analyzing high-dimensional data. These methods mainly focus on variable selection approach, but are limited for purpose of testing with high-dimensional data, and often are required to have explicit derivative likelihood functions. In this dissertation, we propose ``Hybrid Omnibus Test'' for high-dimensional data testing purpose with much less requirements. Our Hybrid Omnibus Test is developed under semi-parametric framework where likelihood function is no longer necessary. Our Hybrid Omnibus Test is a version of Freqentist-Bayesian hybrid score-type test for a functional generalized partial linear single index model, which has link being functional of predictors through a generalized partially linear single index. We propose an efficient score based on estimating equation to the mathematical difficulty in likelihood derivation and construct our Hybrid Omnibus Test. We compare our approach with a empirical likelihood ratio test and Bayesian inference based on Bayes factor using simulation study in terms of false positive rate and true positive rate. Our simulation results suggest that our approach outperforms in terms of false positive rate, true positive rate, and computation cost in high-dimensional case and low-dimensional case. The advantage of our approach is also demonstrated by published biological results with application to a genetic pathway data of type II diabetes.
Ph. D.
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Flament, Guillaume. "Modélisation statistique de l'impact du risque climatique sur la solvabilité des banques." Electronic Thesis or Diss., Rennes, École Nationale de la Statistique et de l'Analyse de l'Information, 2024. http://www.theses.fr/2024NSAIM002.

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Cette thèse propose une adaptation du modèle de Merton-Vasicek. Ce modèle à facteur commun permet de calculer les pertes extrêmes liées à un portefeuille de crédits. Usuellement, ce facteur est considéré comme étant Gaussien et le modèle ne permet pas l’intégration de variables macroéconomiques. Dans ce manuscrit nous proposons de définir les pertes extrêmes à partir des quanti les du facteur commun conditionnels à des variables macroéconomiques. Nous proposons deux types de modèles semi-paramétriques pour es­ timer ces quantités. Nous montrons leur perti­ nence aussi bien sur des exercices numériques que sur des données réelles.Cependant, intégrer le risque climatique dans le calcul de pertes extrêmes nécessite l’introduction de scénarios macroéconomiques qui serviront ensuite de variables explicatives dans ce modèle de Merton-Vasicek. Nous pro­ posons donc d’intégrer des contraintes environ­ nementales dans un IAM, le modèle DICE. En particulier, nous proposons d’intégrer Vexergy dans la modélisation de la productivité totale des facteurs avant d’intégrer cette modélisation pour générer des trajectoires de (dé-)croissance économique, qui pourrait ensuite être inté­ grée comme variable explicative aux modèles précédemment présentés
Tins manuscript proposes an adap­ tation of the Merton-Vasicek model. This single-factor model allows for the calculation of financial losses related to crédit. Typically, this factor is considered Gaussian, and the model do es not allow for the intégration of macroeconomic variables. In this manuscript, we propose to compote this loss based on the quantile of the common factor conditional on macroeconomic variables. We propose two semiparametric types of models to estimate these quantifies. We demonstrate their rele- vance through bot h numerical exercises and real data.However, integrating climate risk into the cal­ culation of extreme losses requires the intro­ duction of macroeconomic scénarios that will then serve as explanatory variables in this Merton-Vasicek model. We hâve therefore pro- posed to integrate environmental constraints into an IAM, the DICE model. Specifically, we propose to integrate exergy into the model­ ing of the Total Factor Productivity (TEP). Next, it is possible to incorporate the pré­ dictions of the TFP to generate trajectories of (de-)growth that could eventually be inte- grated as explanatory variables into the condi­ tional quantile models
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Knefati, Muhammad Anas. "Estimation non-paramétrique du quantile conditionnel et apprentissage semi-paramétrique : applications en assurance et actuariat." Thesis, Poitiers, 2015. http://www.theses.fr/2015POIT2280/document.

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La thèse se compose de deux parties : une partie consacrée à l'estimation des quantiles conditionnels et une autre à l'apprentissage supervisé. La partie "Estimation des quantiles conditionnels" est organisée en 3 chapitres : Le chapitre 1 est consacré à une introduction sur la régression linéaire locale, présentant les méthodes les plus utilisées, pour estimer le paramètre de lissage. Le chapitre 2 traite des méthodes existantes d’estimation nonparamétriques du quantile conditionnel ; Ces méthodes sont comparées, au moyen d’expériences numériques sur des données simulées et des données réelles. Le chapitre 3 est consacré à un nouvel estimateur du quantile conditionnel et que nous proposons ; Cet estimateur repose sur l'utilisation d'un noyau asymétrique en x. Sous certaines hypothèses, notre estimateur s'avère plus performant que les estimateurs usuels. La partie "Apprentissage supervisé" est, elle aussi, composée de 3 chapitres : Le chapitre 4 est une introduction à l’apprentissage statistique et les notions de base utilisées, dans cette partie. Le chapitre 5 est une revue des méthodes conventionnelles de classification supervisée. Le chapitre 6 est consacré au transfert d'un modèle d'apprentissage semi-paramétrique. La performance de cette méthode est montrée par des expériences numériques sur des données morphométriques et des données de credit-scoring
The thesis consists of two parts: One part is about the estimation of conditional quantiles and the other is about supervised learning. The "conditional quantile estimate" part is organized into 3 chapters. Chapter 1 is devoted to an introduction to the local linear regression and then goes on to present the methods, the most used in the literature to estimate the smoothing parameter. Chapter 2 addresses the nonparametric estimation methods of conditional quantile and then gives numerical experiments on simulated data and real data. Chapter 3 is devoted to a new conditional quantile estimator, we propose. This estimator is based on the use of asymmetrical kernels w.r.t. x. We show, under some hypothesis, that this new estimator is more efficient than the other estimators already used. The "supervised learning" part is, too, with 3 chapters: Chapter 4 provides an introduction to statistical learning, remembering the basic concepts used in this part. Chapter 5 discusses the conventional methods of supervised classification. Chapter 6 is devoted to propose a method of transferring a semiparametric model. The performance of this method is shown by numerical experiments on morphometric data and credit-scoring data
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Conference papers on the topic "Semi-parametric single index"

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Johnson, Shane, and Tanzeel Ur Rehman. "Design and Development of an Adjustable Constant Force Mechanism." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-114438.

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Abstract “Constant force mechanisms (CFMs) are widely used in various applications, including force regulators, surgical graspers, grippers, micro injectors, vibration isolation and constant force actuators. CFMs, designed for a single constant force, necessitate active mechanical control for force adjustability. The objective of this paper is to develop a semi-active, structural controlled adjustable CFM (ACFM) with a large change in constant force. A path based structural optimization is conducted using graphs. A 2 × 2 grid is selected, and nodal locations are perturbed to obtain a large stroke CFM with high quality constant force as defined by the energy similarity index, i.e., ratio of the energy capacity of the CFM to an ideal CFM. The slenderness of the CFM was tuned in order to change the constant force of the mechanism using a parametric study. The parametric study showed a change in constant force, maximum stroke and the quality of constant force as the slenderness was changed. In the experiment using a 2-layer unidirectional fiberglass specimen, slenderness was tuned by modifying a fixed boundary condition location in a fixed-fixed setup. The constant force (CF) was doubled from its value while maintaining a high energy similarity index of 0.9. Compared to mechanisms in the literature, the selected proposed mechanism was found to be 4.1% more compact. In conclusion, semi active control using boundary condition location adjustment in CFM was implemented for the first time to develop an ACFM. The ACFM is ideal for diverse applications including impact attenuation, vibration isolation, and surgical graspers.”
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