Estimation for semiparametric varying coefficient models with different smoothing variables under random right censoring

Yang, Seong J.

doi:10.1016/j.jkss.2017.12.001

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…Here, Π(H) denotes the projection onto the space H. In order to prove the main theorems, we first list some regularity conditions which are used in this paper. ese conditions are mild and also assumed in [16].…”

Section: Proof Of the Main Resultsmentioning

confidence: 99%

“…For the given β, α(•) can be estimated by using some smoothing techniques with response Y G,i − X T i β. Here, we use the smoothing backfitting technique studied in Lee et al [25] and Yang [16] and denote the resultant estimator as α G (•; β). Let α ll G,j (•; β) satisfy the following system of integral equations:…”

Section: Empirical Likelihood For Right Censored Datamentioning

confidence: 99%

“…For example, it may be the identity function or I(• ≤ t), which produces the conditional mean or the conditional probability function given covariates, respectively. When the response was random right censored, Buckley and James [14] gave a method of estimating parameters in the linear regression model; Bravo [15] considered the problem of estimation and inference in semiparametric varying-coefficients partially linear models; for semiparametric varying-coefficient models with different smoothing variables, Yang [16] applied a mean-preserving transformation method to construct estimators for unknown parameters and functions and established their asymptotic normalities. It is well known that the empirical likelihood (EL) method proposed by Owen [17] has many advantages over the normal approximation (NA) method and the bootstrap methods for constructing confidence intervals.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data

Zhong

Fan

2020

Discrete Dynamics in Nature and Society

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Empirical likelihood as a nonparametric approach has been demonstrated to have many desirable merits for constructing a confidence region. The purpose of this article is to apply the empirical likelihood method to study the generalized functional-coefficient regression models with multiple smoothing variables when the response is subject to random right censoring. The coefficient functions with multiple smoothing variables can accommodate various nonlinear interaction effects between covariates. The empirical log-likelihood ratio of an unknown parameter is constructed and shown to have a standard chi-squared limiting distribution at the true parameter. Based on this, the confidence region of the unknown parameter can be constructed. Simulation studies are carried out to indicate that the empirical likelihood method performs better than a normal approximation-based approach for constructing the confidence region.

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Section: Proof Of the Main Resultsmentioning

confidence: 99%

Section: Empirical Likelihood For Right Censored Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data

Zhong

Fan

2020

Discrete Dynamics in Nature and Society

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“…At present, there are two approaches to solve the problem. One is the marginal integration technique [31][32][33], the other is the smooth backfitting [34,35].…”

Section: Estimation Methodsmentioning

confidence: 99%

Measurement and Analysis of Contribution Rate for China Rice Input Factors via a Varying-Coefficient Production Function Model

Wang

et al. 2022

Agriculture

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To explore the internal driving force of the growth of rice yield per unit area in China, a model based on varying-coefficient production function is proposed in this study, which comes from the idea that the constant elasticity parameters in the Cobb-Douglas production function can be extended to functional forms. Applying such model to economic growth analysis, on the one hand, the dynamic contribution rate of each input factor can be measured, and, on the other hand, the contribution rate of the input factor can be decomposed into net factor contribution rate and interaction factor contribution rate, thus expanding the explanatory ability of growth rate equation. Using such model, the output elasticity of capital and labor in China’s rice yield growth are calculated from 1978 to 2020, and the dynamic characteristics of the contribution rate of capital, labor and generalized technological progress are analyzed. Next, the capital contribution rate is decomposed according to the composition of the total capital. The results show that: (1) The capital elasticity and labor elasticity are indeed not constant in different years. In China, from 1978 to 2020 the value of capital elasticity was between 0.3209 to 0.3589, with a mean of 0.3437, and the value of labor elasticity was between −0.1759 to −0.1640, with a mean of −0.1730. (2) Natural disasters do affect capital elasticity and labor elasticity in rice production. (3) When the annual proportion of crop disasters increases, the contribution rate of interaction between capital and natural disaster (KDR) value is negative, whereas the contribution rate of interaction between labor and natural disaster (LDR) value is positive. (4) Compared with 1978, the generalized technological progress contribution rate (GTPR) of the rice yield growth in China from 1979 to 2020 shows a declining trend in fluctuations, whereas the total capital contribution rate (TKR) shows a rising trend in fluctuations and the total labor contribution rate (TLR) is relatively stable in the same period. Since 2000, capital investment has become the main factor for the rice yield growth per unit area in China, of which machinery, chemical fertilizer, seed and pesticide are the four most important input factors.

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“…), Zhang and Li [8] employed the local linear technique to obtain an initial value for the estimate of each coefficient function in model (1), and, then, the integrated estimate of each coefficient function is defined by integrating its initial value on these variables which the coefficient function does not share; Zhang and Li [9] used the local linear technique to give an initial value for every estimate of the coefficient function in model (1). Then the averaged estimate of each coefficient function 2 Discrete Dynamics in Nature and Society is defined by averaging its initial value on these variables which the coefficient function does not share; Yang [10] proposed estimators for this model under random right censoring case by using mean-preserving transformation and established their asymptotic properties. The estimation procedure is based on the profiling and the smooth backfitting techniques.…”

Section: Introductionmentioning

confidence: 99%

Averaged and Integrated Estimations of Varying-Coefficient Regression Models with Dependent Observations

Fan

2019

Discrete Dynamics in Nature and Society

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In practical applications, lots of data such as sequentially collected economic data often exhibit some evident dependence. This paper studies the varying-coefficient regression models with different smoothing variables when the data form a stationarymixing sequence. Both the averaged and integrated estimators of coefficient functions are proposed. The asymptotic normalities of the proposed averaged and integrated estimators are also established.

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Estimation for semiparametric varying coefficient models with different smoothing variables under random right censoring

Cited by 4 publications

References 19 publications

Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data

Empirical Likelihood for Generalized Functional-Coefficient Regression Models with Multiple Smoothing Variables under Right Censoring Data

Measurement and Analysis of Contribution Rate for China Rice Input Factors via a Varying-Coefficient Production Function Model

Averaged and Integrated Estimations of Varying-Coefficient Regression Models with Dependent Observations

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