Topic
Time point
About: Time point is a research topic. Over the lifetime, 480 publications have been published within this topic receiving 2486 citations.
Papers published on a yearly basis
Papers
TL;DR: A class of regression models is studied, which directly relates the RMET to its "baseline" covariates for predicting the future subjects' RMETs, and a cross-validation procedure is utilized to select the "best" among all the working models considered in the model building and evaluation process.
Abstract: For designing, monitoring, and analyzing a longitudinal study with an event time as the outcome variable, the restricted mean event time (RMET) is an easily interpretable, clinically meaningful summary of the survival function in the presence of censoring The RMET is the average of all potential event times measured up to a time point τ and can be estimated consistently by the area under the Kaplan-Meier curve over $[0, \tau ]$ In this paper, we study a class of regression models, which directly relates the RMET to its "baseline" covariates for predicting the future subjects' RMETs Since the standard Cox and the accelerated failure time models can also be used for estimating such RMETs, we utilize a cross-validation procedure to select the "best" among all the working models considered in the model building and evaluation process Lastly, we draw inferences for the predicted RMETs to assess the performance of the final selected model using an independent data set or a "hold-out" sample from the original data set All the proposals are illustrated with the data from the an HIV clinical trial conducted by the AIDS Clinical Trials Group and the primary biliary cirrhosis study conducted by the Mayo Clinic
203 citations
TL;DR: This study creates an adaptive procedure for sequential forecasting of incident duration that includes two adaptive Artificial Neural Network-based models as well as the data fusion techniques to forecast incident duration.
130 citations
TL;DR: In this article, the authors proposed a new approach to modeling and forecasting of non-stationary time series with applications to volatility modeling for financial data. But their approach is based on the assumption of local homogeneity: for every time point, there exists a historical interval of homogerzeity, in which the volatility parameter can be well approximated by a constant.
Abstract: This paper offers a new approach to modeling and forecasting of non-stationary time series with applications to volatility modeling for financial data. The approach is based on the assumption of local homogeneity: for every time point, there exists a historical interval of homogerzeity, in which the volatility parameter can be well approximated by a constant. The proposed procedure recovers this interval from the data using the local change point (LCP) analysis. Afterward, the estimate of the volatility can be simply obtained by local averaging. The approach carefully addresses the question of choosing the tuning parameters of the procedure using the so-called "propagation" condition. The main result claims a new "oracle" inequality in terms of the modeling bias which measures the quality of the local constant approximation. This result yields the optimal rate of estimation for smooth and piecewise constant volatility functions. Then, the new procedure is applied to some data sets and a comparison with a standard GARCH model is also provided. Finally, we discuss applications of the new method to the Value at Risk problem. The numerical results demonstrate a very reasonable performance of the new method.
128 citations
TL;DR: In this paper, the analysis of event count data in which each subject is observed at only one time point and no information is available on subjects between their entry time and observation points is considered.
Abstract: This article considers the analysis of event count data in which each subject is observed at only one time point and no information is available on subjects between their entry time and observation points. This type of data, often referred to as current status data, arises frequently—for example, in demography, epidemiology, and reliability studies. Statistical methods for the analysis of current status data from point processes are proposed. Specifically, a statistic for testing the equality of the mean functions of point processes is presented and its asymptotic distribution obtained. For illustration, the proposed method is used to analyze multiple tumor data from a tumorgenicity experiment, with focus on the comparison of tumor growth rates in male and female rats. The adequacy of the asymptotic distribution of the test statistic is evaluated in a small simulation study. Power comparisons with the usual parametric model are also obtained. Finally, some possible directions for further research...
74 citations
TL;DR: In this article, an interval process model is proposed for time-varying or dynamic uncertainty analysis when information of the uncertain parameter is inadequate. And the structural dynamic responses are derived in the form of upper and lower bounds, providing an important guidance for practical safety analysis and reliability design of structures.
68 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 12 |
| 2024 | 11 |
| 2023 | 26 |
| 2022 | 52 |
| 2021 | 19 |
| 2020 | 41 |