Correcting treatment effect for treatment switching in randomized oncology trials with a modified iterative parametric estimation method

2016 ◽  
Vol 35 (21) ◽  
pp. 3690-3703 ◽  
Author(s):  
Jin Zhang ◽  
Cong Chen
Keyword(s):  
Treatment Effect ◽  
Estimation Method ◽  
Parametric Estimation ◽  
Treatment Switching

Reliability assessment of offshore dynamic scour protections using copulas

2018 ◽  
Vol 43 (5) ◽  
pp. 506-538 ◽  
Author(s):  
T Fazeres-Ferradosa ◽  
F Taveira-Pinto ◽  
X Romão ◽  
MT Reis ◽  
L das Neves
Keyword(s):  
Wind Farm ◽  
Estimation Method ◽  
Information Criterion ◽  
Probability Of Failure ◽  
Parametric Estimation ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Spectral Parameters ◽  
Peak Period ◽  
Kendall’S Τ

This article presents a methodology to assess the reliability of dynamic scour protections used to protect offshore wind turbine foundations. The computed probabilities of failure are based on a dataset of 124 months of hindcast data from the Horns Rev 3 offshore wind farm. Copula-based models are used to obtain the joint distribution function of the significant wave height and spectral peak period and to obtain the probability of failure of scour protections. The sensitivity of the probability of failure to each model is addressed. The influence of the duration of the waves’ time series is also studied. A sensitivity analysis of the probability of failure to physical constraints, such as the water depth, current’s velocity or the mean diameter of the armour units, is performed. The results show that probability of failure is dependent on the copula used to model the spectral parameters and the associated value of Kendall’s τ. It is shown that the copula presenting the best values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) did not lead to the probabilities of failure that are closer to the non-parametric estimation, obtained by means of the bivariate version of the Kernel density estimation method. The application to the case study led to annual probabilities of failure, which are comparable with the values applied for other offshore components, according to the current offshore wind industry standards.

Parametric estimation in boolean segment processes by a restoration-estimation method

1998 ◽  
Vol 30 (2) ◽  
pp. 274-275
Author(s):  
Joël Chadœuf ◽  
Rachid Senoussi ◽  
Jian-Feng Yao
Keyword(s):  
Estimation Method ◽  
Parametric Estimation

Research and Extended of Semi-Parametric Model Additional System Parameter

2014 ◽  
Vol 580-583 ◽  
pp. 2815-2819
Author(s):  
You Ping Wu ◽  
Chun Tao Wang ◽  
Jia Bang Wang
Keyword(s):  
Calculation Method ◽  
System Parameter ◽  
Mixed Model ◽  
Estimation Method ◽  
Parametric Model ◽  
Parametric Estimation ◽  
Accuracy Evaluation ◽  
Estimation Of Parameters ◽  
System Parameters ◽  
Regularization Matrix

In this paper, a new solution to the semi-parametric estimation of a mixed model additional system parameters was conducted to derive a calculation method of parameter adjustment at the model regularization matrix, and determine the estimation of parameters and non-parameters as well as the accuracy evaluation formula of the model. The effectiveness of the semi-parametric estimation method was demonstrated through simulation examples, and the semi-parametric model additional system parameters was further extended.

scholarly journals Parameter estimation in a Black Scholes

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 117-122
Author(s):  
Mustafa Bayram ◽  
Buyukoz Orucova ◽  
Tugcem Partal
Keyword(s):  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Parametric Method ◽  
Estimation Method ◽  
Parametric Estimation ◽  
Likelihood Method ◽  
Observation Data ◽  
Unknown Parameters ◽  
Black Scholes ◽  
Non Parametric

In this paper we discuss parameter estimation in black scholes model. A non-parametric estimation method and well known maximum likelihood estimator are considered. Our aim is to estimate the unknown parameters for stochastic differential equation with discrete time observation data. In simulation study we compare the non-parametric method with maximum likelihood method using stochastic numerical scheme named with Euler Maruyama.

SURVIVAL COPULA PARAMETERS ESTIMATION FOR ARCHIMEDEAN FAMILY UNDER SINGLY CENSORING

2021 ◽  
Vol 10 (6) ◽  
pp. 2847-2864
Author(s):  
N. Idiou ◽  
F. Benatia
Keyword(s):  
Diabetic Retinopathy ◽  
Simulation Study ◽  
Estimation Method ◽  
Relative Bias ◽  
Parameters Estimation ◽  
Parametric Estimation ◽  
Lifetime Data ◽  
Archimedean Copulas ◽  
Theoretical Results ◽  
Survival Copula

Given $(Z_{i},\delta _{i})=\left\{ \min (T_{i},C_{i}),I_{(T_{i}<C_{i})_{i=1,2}}\right\} ,$ as dependent or independent right-censored variables, general formulas are proven for a semi-parametric estimation of the proposed method. As a logical continuation of results established by N.IDIOU et al 2021 \cite{ref16}, a new estimator of $\tilde{C}$ is proposed by considering that the underlying copula is Archimedean, under singly censoring data. As an application, two Archimedean copulas models have been chosen to illustrate our theoretical results. A simulation study follows, which sheds light on the behavior of the process estimation method shown that the proposed estimator performs well in terms of relative bias and RMSE. The methodology of the proposed estimator is also illustrated by using lifetime data from the Diabetic Retinopathy Study, where its efficiency and robustness are observed.

Adjusting for treatment switching in randomised controlled trials – A simulation study and a simplified two-stage method

2014 ◽  
Vol 26 (2) ◽  
pp. 724-751 ◽  
Author(s):  
Nicholas R Latimer ◽  
KR Abrams ◽  
PC Lambert ◽  
MJ Crowther ◽  
AJ Wailoo ◽  
...  
Keyword(s):  
Treatment Effect ◽  
Survival Benefit ◽  
Randomised Controlled Trials ◽  
Health Technology ◽  
Control Group ◽  
Controlled Trials ◽  
Inverse Probability ◽  
Treatment Switching ◽  
Overall Survival Benefit ◽  
Randomised Controlled

Estimates of the overall survival benefit of new cancer treatments are often confounded by treatment switching in randomised controlled trials (RCTs) – whereby patients randomised to the control group are permitted to switch onto the experimental treatment upon disease progression. In health technology assessment, estimates of the unconfounded overall survival benefit associated with the new treatment are needed. Several switching adjustment methods have been advocated in the literature, some of which have been used in health technology assessment. However, it is unclear which methods are likely to produce least bias in realistic RCT-based scenarios. We simulated RCTs in which switching, associated with patient prognosis, was permitted. Treatment effect size and time dependency, switching proportions and disease severity were varied across scenarios. We assessed the performance of alternative adjustment methods based upon bias, coverage and mean squared error, related to the estimation of true restricted mean survival in the absence of switching in the control group. We found that when the treatment effect was not time-dependent, rank preserving structural failure time models (RPSFTM) and iterative parameter estimation methods produced low levels of bias. However, in the presence of a time-dependent treatment effect, these methods produced higher levels of bias, similar to those produced by an inverse probability of censoring weights method. The inverse probability of censoring weights and structural nested models produced high levels of bias when switching proportions exceeded 85%. A simplified two-stage Weibull method produced low bias across all scenarios and provided the treatment switching mechanism is suitable, represents an appropriate adjustment method.

Parametric estimation of phase and group slownesses from sonic logging waveforms

Geophysics ◽  
1992 ◽  
Vol 57 (8) ◽  
pp. 978-985 ◽  
Author(s):  
Kai Hsu ◽  
Cengiz Esmersoy
Keyword(s):  
Parametric Method ◽  
Estimation Method ◽  
Parametric Estimation ◽  
Flexural Wave ◽  
Smooth Functions ◽  
Dispersive Waves ◽  
Prony Method ◽  
Receiver Array ◽  
Frequency Independent ◽  
Sonic Logging

Sonic logging waveforms consist of a mixture of nondispersive waves, such as the P‐ and S‐headwaves, and dispersive waves, such as the Stoneley and pseudo‐Rayleigh waves in monopole logging and the flexural wave in dipole logging. Conventionally, slowness dispersion curves of various waves are estimated at each frequency, independent of data at other frequencies. This approach does not account for the fact that slowness dispersion functions in sonic logging are continuous and, in most cases, smooth functions of frequency. We describe a parametric slowness estimation method that uses this property by locally approximating the wavenumber of each wave as a linear function of frequency. This provides a parametric model for the phase and group slownesses of the waves propagating across the receiver array. The estimation of phase and group slownesses is then carried out by minimizing the squared difference between the predicted and observed waveforms. The minimization problem is nonlinear and is solved by an iterative algorithm. Examples using synthetic and field data are shown and the results are compared with those obtained by the conventional Prony method. Based on the comparison, we conclude that the parametric method is better than the conventional Prony method in providing robust and stable slowness estimates.

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