On semiparametric modelling, estimation and inference for survival data subject to dependent censoring

Biometrika ◽  
2020 ◽  
Author(s):  
N W Deresa ◽  
I Van Keilegom
Keyword(s):  
Linear Model ◽  
Survival Data ◽  
Estimation Method ◽  
Bivariate Normal Distribution ◽  
Dependent Censoring ◽  
Transformation Model ◽  
Model Parameters ◽  
Finite Sample ◽  
Asymptotically Normal ◽  
Nonparametric Transformation

Abstract When modelling survival data, it is common to assume that the survival time T is conditionally independent of the censoring time C given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model, which assumes that after a proper nonparametric monotone transformation, the vector (T, C) follows a linear model, and the vector of errors in this bivariate linear model follows a standard bivariate normal distribution with possibly non-diagonal covariance matrix. We show that this semiparametric model is identifiable, and propose estimators of the nonparametric transformation, the regression coefficients and the correlation between the error terms. It is shown that the estimators of the model parameters and the transformation are consistent and asymptotically normal. We also assess the finite sample performance of the proposed method by comparing it with an estimation method under a fully parametric model. Finally, our method is illustrated using data from the AIDS Clinical Trial Group 175 study.

Motion Model Parameters Estimation for Electronic Image Stabilization Instrument

2013 ◽  
Vol 325-326 ◽  
pp. 1543-1546
Author(s):  
Xun Yu Zhong ◽  
Tian Hui Ren
Keyword(s):  
Motion Estimation ◽  
Least Squares Method ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Block Matching ◽  
Parameters Estimation ◽  
Transformation Model ◽  
Image Motion ◽  
Model Parameters ◽  
Image Stabilization

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

Model Parameters Estimation for Image Motion Compensation

2013 ◽  
Vol 347-350 ◽  
pp. 3672-3676
Author(s):  
Xun Yu Zhong ◽  
Xiao Shan Li ◽  
Yong Gang Zhao
Keyword(s):  
Motion Estimation ◽  
Least Squares Method ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Block Matching ◽  
Parameters Estimation ◽  
Transformation Model ◽  
Image Motion ◽  
Model Parameters ◽  
Macro Block

Fast and optimal motion estimation method is proposed for electronic image stabilization. First, an approach for macro-block judgment is presented. Before motion vectors calculation, gradient information is analyzed, only useful reference blocks that are indispensable for accurate motion estimation are selected, by which the number of macro-blocks for subsequent calculation is reduced. Second, in the block matching, an improved SSDA is used to reduce computing cost. Finally, the affine transformation model and similarity transformation model of image motion are created and using least squares method for solving the optimal estimation of model parameters. Experimental results show the accuracy and fast computing speed of the proposed method.

scholarly journals POT-Based Estimation of the Renewal Function of Interoccurrence Times of Heavy-Tailed Risks

2010 ◽  
Vol 2010 ◽  
pp. 1-14 ◽  
Author(s):  
Abdelhakim Necir ◽  
Abdelaziz Rassoul ◽  
Djamel Meraghni
Keyword(s):  
Simulation Study ◽  
Estimation Method ◽  
Infinite Variance ◽  
Finite Sample ◽  
Renewal Function ◽  
Peaks Over Threshold ◽  
Confidence Bounds ◽  
Asymptotically Normal ◽  
Semiparametric Estimator ◽  
Heavy Tailed

Making use of the peaks over threshold (POT) estimation method, we propose a semiparametric estimator for the renewal function of interoccurrence times of heavy-tailed insurance claims with infinite variance. We prove that the proposed estimator is consistent and asymptotically normal, and we carry out a simulation study to compare its finite-sample behavior with respect to the nonparametric one. Our results provide actuaries with confidence bounds for the renewal function of dangerous risks.

scholarly journals ESTIMATION OF A SEMIPARAMETRIC TRANSFORMATION MODEL IN THE PRESENCE OF ENDOGENEITY

2018 ◽  
Vol 35 (1) ◽  
pp. 73-110 ◽  
Author(s):  
Anne Vanhems ◽  
Ingrid Van Keilegom
Keyword(s):  
Control Function ◽  
Estimation Method ◽  
Regression Function ◽  
Transformation Model ◽  
Regularity Conditions ◽  
Explanatory Variables ◽  
Asymptotically Normal ◽  
Semiparametric Transformation Model ◽  
Semiparametric Transformation ◽  
Profile Estimation

We consider a semiparametric transformation model, in which the regression function has an additive nonparametric structure and the transformation of the response is assumed to belong to some parametric family. We suppose that endogeneity is present in the explanatory variables. Using a control function approach, we show that the proposed model is identified under suitable assumptions, and propose a profile estimation method for the transformation. The proposed estimator is shown to be asymptotically normal under certain regularity conditions. A simulation study shows that the estimator behaves well in practice. Finally, we give an empirical example using the U.K. Family Expenditure Survey.

scholarly journals Semiparametric likelihood inference for heterogeneous survival data under double truncation based on a Poisson birth process

2021 ◽  
Author(s):  
Achim Dörre
Keyword(s):  
Survival Data ◽  
Process Model ◽  
Optimization Method ◽  
Finite Sample ◽  
Birth Process ◽  
Dimensional Parameter ◽  
Lifetime Distributions ◽  
Computational Performance ◽  
Semiparametric Likelihood ◽  
Double Truncation

AbstractWe study a selective sampling scheme in which survival data are observed during a data collection period if and only if a specific failure event is experienced. Individual units belong to one of a finite number of subpopulations, which may exhibit different survival behaviour, and thus cause heterogeneity. Based on a Poisson process model for individual emergence of population units, we derive a semiparametric likelihood model, in which the birth distribution is modeled nonparametrically and the lifetime distributions parametrically, and define maximum likelihood estimators. We propose a Newton–Raphson-type optimization method to address numerical challenges caused by the high-dimensional parameter space. The finite-sample properties and computational performance of the proposed algorithms are assessed in a simulation study. Personal insolvencies are studied as a special case of double truncation and we fit the semiparametric model to a medium-sized dataset to estimate the mean age at insolvency and the birth distribution of the underlying population.

Model of the influence of Internet finance on monetary policy based on gibbs sampling and vector autoregression

2020 ◽  
pp. 1-11
Author(s):  
Hui Wang ◽  
Huang Shiwang
Keyword(s):  
Monetary Policy ◽  
Gibbs Sampling ◽  
Posterior Distribution ◽  
Estimation Method ◽  
Financial Supervision ◽  
Var Model ◽  
Model Parameters ◽  
State Variables ◽  
Frequency Data ◽  
Internet Finance

The various parts of the traditional financial supervision and management system can no longer meet the current needs, and further improvement is urgently needed. In this paper, the low-frequency data is regarded as the missing of the high-frequency data, and the mixed frequency VAR model is adopted. In order to overcome the problems caused by too many parameters of the VAR model, this paper adopts the Bayesian estimation method based on the Minnesota prior to obtain the posterior distribution of each parameter of the VAR model. Moreover, this paper uses methods based on Kalman filtering and Kalman smoothing to obtain the posterior distribution of latent state variables. Then, according to the posterior distribution of the VAR model parameters and the posterior distribution of the latent state variables, this paper uses the Gibbs sampling method to obtain the mixed Bayes vector autoregressive model and the estimation of the state variables. Finally, this article studies the influence of Internet finance on monetary policy with examples. The research results show that the method proposed in this article has a certain effect.

scholarly journals Industrial Plume Properties Retrieved by Optimal Estimation Using Combined Hyperspectral and Sentinel-2 Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1865
Author(s):  
Gabriel Calassou ◽  
Pierre-Yves Foucher ◽  
Jean-François Léon
Keyword(s):  
Matched Filter ◽  
Estimation Method ◽  
Optimal Estimation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Sequential Method ◽  
Modal Radius ◽  
Sentinel 2

Stack emissions from the industrial sector are a subject of concern for air quality. However, the characterization of the stack emission plume properties from in situ observations remains a challenging task. This paper focuses on the characterization of the aerosol properties of a steel plant stack plume through the use of hyperspectral (HS) airborne remote sensing imagery. We propose a new method, based on the combination of HS airborne acquisition and surface reflectance imagery derived from the Sentinel-2 Multi-Spectral Instrument (MSI). The proposed method detects the plume footprint and estimates the surface reflectance under the plume, the aerosol optical thickness (AOT), and the modal radius of the plume. Hyperspectral surface reflectances are estimated using the coupled non-negative matrix factorization (CNMF) method combining HS and MSI data. The CNMF reduces the error associated with estimating the surface reflectance below the plume, particularly for heterogeneous classes. The AOT and modal radius are retrieved using an optimal estimation method (OEM), based on the forward model and allowing for uncertainties in the observations and in the model parameters. The a priori state vector is provided by a sequential method using the root mean square error (RMSE) metric, which outperforms the previously used cluster tuned matched filter (CTMF). The OEM degrees of freedom are then analysed, in order to refine the mask plume and to enhance the quality of the retrieval. The retrieved mean radii of aerosol particles in the plume is 0.125 μμm, with an uncertainty of 0.05 μμm. These results are close to the ultra-fine mode (modal radius around 0.1 μμm) observed from in situ measurements within metallurgical plant plumes from previous studies. The retrieved AOT values vary between 0.07 (near the source point) and 0.01, with uncertainties of 0.005 for the darkest surfaces and above 0.010 for the brightest surfaces.

scholarly journals Experimental Verification of Real-Time Flow-Rate Estimations in a Tilting-Ladle-Type Automatic Pouring Machine

2021 ◽  
Vol 11 (15) ◽  
pp. 6701
Author(s):  
Yuta Sueki ◽  
Yoshiyuki Noda
Keyword(s):  
Real Time ◽  
Molten Metal ◽  
Flow Rate ◽  
Kalman Filters ◽  
Estimation Method ◽  
Estimation Methods ◽  
Liquid Volume ◽  
Model Parameters ◽  
Rate Estimation ◽  
Time Flow

This paper discusses a real-time flow-rate estimation method for a tilting-ladle-type automatic pouring machine used in the casting industry. In most pouring machines, molten metal is poured into a mold by tilting the ladle. Precise pouring is required to improve productivity and ensure a safe pouring process. To achieve precise pouring, it is important to control the flow rate of the liquid outflow from the ladle. However, due to the high temperature of molten metal, directly measuring the flow rate to devise flow-rate feedback control is difficult. To solve this problem, specific flow-rate estimation methods have been developed. In the previous study by present authors, a simplified flow-rate estimation method was proposed, in which Kalman filters were decentralized to motor systems and the pouring process for implementing into the industrial controller of an automatic pouring machine used a complicatedly shaped ladle. The effectiveness of this flow rate estimation was verified in the experiment with the ideal condition. In the present study, the appropriateness of the real-time flow-rate estimation by decentralization of Kalman filters is verified by comparing it with two other types of existing real-time flow-rate estimations, i.e., time derivatives of the weight of the outflow liquid measured by the load cell and the liquid volume in the ladle measured by a visible camera. We especially confirmed the estimation errors of the candidate real-time flow-rate estimations in the experiments with the uncertainty of the model parameters. These flow-rate estimation methods were applied to a laboratory-type automatic pouring machine to verify their performance.

scholarly journals A semiparametric linear transformation model to estimate causal effects for survival data

2013 ◽  
Vol 42 (1) ◽  
pp. 18-35 ◽  
Author(s):  
Huazhen Lin ◽  
Yi Li ◽  
Liang Jiang ◽  
Gang Li
Keyword(s):  
Linear Transformation ◽  
Survival Data ◽  
Transformation Model ◽  
Causal Effects ◽  
Linear Transformation Model
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