About: Sequential estimation is a research topic. Over the lifetime, 1996 publications have been published within this topic receiving 44938 citations.
Papers published on a yearly basis
••08 Nov 2004
TL;DR: The motivation, development, use, and implications of the UT are reviewed, which show it to be more accurate, easier to implement, and uses the same order of calculations as linearization.
Abstract: The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.
01 Jan 1977
TL;DR: A simple and efficient method of estimating points on the psychometric function, and thus of estimating absolute and difference limens, is described.
Abstract: A simple and efficient method of estimating points on the psychometric function, and thus of estimating absolute and difference limens, is described. An illustration of the method is given in which sensitivity to inter-aural time differences is measured.
•15 Sep 1999
TL;DR: A short history of sequential and group sequential methods can be found in this paper, where the authors present a road map for the application of two-sided tests for comparing two treatments with normal response of known variance.
Abstract: INTRODUCTION About This Book Why Sequential Methods A Short History of Sequential and Group Sequential Methods Chapter Organization: A Roadmap Bibliography and Notes TWO-SIDED TESTS: INTRODUCTION Two-Sided Tests for Comparing Two Treatments with Normal Response of Known Variance A Fixed Sample Test Group Sequential Tests Pocock's Test O'Brien and Fleming's Test Properties of Pocock and O'Brien and Fleming Tests Other Tests Conclusions TWO-SIDED TESTS: GENERAL APPLICATIONS A Unified Formulation Applying the Tests with Equal Group Sizes Applying the Tests with Unequal Increments in Information Normal Linear Models Other Parametric Models Binary Data: Group Sequential Tests for Proportions The Group Sequential Log-Rank Test for Survival Data Group Sequential t-Tests ONE-SIDED TESTS Introduction The Power Family of One-Sided Group Sequential Tests Adapting Power Family Tests to Unequal Increments in Information Group Sequential One-Sided t-Tests Whitehead's Triangular Test TWO-SIDED TESTS WITH EARLY STOPPING UNDER THE NULL HYPOTHESIS Introduction The Power Family of Two-Sided, Inner Wedge Tests Whitehead's Double Triangular Test EQUIVALENCE TESTS Introduction One-Sided Tests of Equivalence Two-Sided Tests of Equivalence: Application to Comparative Bioavailability Studies Individual Bioequivalence: A One-Sided Test for Proportions Bibliography and Notes FLEXIBLE MONITORING: THE ERROR SPENDING APPROACH Unpredictable Information Sequences Two-Sided Tests One-Sided Tests Data Dependent Timing of Analyses Computations for Error Spending Tests ANALYSIS FOLLOWING A SEQUENTIAL TEST Introduction Distribution Theory Point Estimation P-Values Confidence intervals REPEATED CONFIDENCE INTERVALS Introduction Example: Difference of Normal Means Derived Tests: Use of RCIs to Aid Early Stopping Decisions Repeated P-Values Discussion STOCHASTIC CURTAILMENT Introduction Conditional Power Approach Predictive Power Approach A Parameter-Free Approach A Case Study with Survival Data Bibliography and Notes GENERAL GROUP SEQUENTIAL DISTRIBUTION THEORY Introduction A Standard Joint Distribution for Successive Estimates of a Parameter Vector Normal Linear Models Normal Linear Models with Unknown Variance: Group Sequential t-Tests Example: An Exact One-Sample Group Sequential t-Test General Parametric Models: Generalized Linear Models Connection with Survival Analysis BINARY DATA A Single Bernoulli Probability Two Bernoulli Probabilities The Odds Ratio and Multiple 2 x 2 Tables Case-Control and Matched Pair Analysis Logistic Regression: Adjusting for Covariates Bibliography and Notes SURVIVAL DATA Introduction The Log Rank Test The Stratified Log-Rank Test Group Sequential Methods for Survival Data with Covariates Repeated Confidence Intervals for a Hazard Ratio Example: A Clinical Trial for Carcinoma of the Oropharynx Survival Probabilities and Quantiles Bibliography and Notes INTERNAL PILOT STUDIES: SAMPLE SIZE RE-ESTIMATION The Role of an Internal Pilot Phase Sample Size Re-estimation for a Fixed Sample Test Sample Size Re-estimation in Group Sequential Tests MULTIPLE ENDPOINTS Introduction The Bonferroni Procedure A Group Sequential Hotelling Test A Group Sequential Version of O'Brien's Test Tests Based on other Global Statistics Tests Based on Marginal Criteria Bibliography and Notes MULTI-ARMED TRIALS Introduction Global Tests Monitoring Pairwise Comparisons Bibliography and Notes ADAPTIVE TREATMENT ASSIGNMENT A Multi-Stage Adaptive Design A Multi-Stage Adaptive Design with Time Trends Validity of Adaptive Multi-Stage Procedures Bibliography and Notes BAYESIAN APPROACHES The Bayesian Paradigm Stopping Rules Choice of Prior Distribution Discussion NUMERICAL COMPUTATIONS FOR GROUP SEQUENTIAL TESTS Introduction The Basic Calculation Error Probabilities and Sample Size Distributions Tests Defined by Error Spending Functions Analysis Following a Group Sequential Test Further Applications of Numerical Computation Computer Software
TL;DR: A dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model.
Abstract: Hydrologic models are twofold: models for understanding physical processes and models for prediction. This study addresses the latter, which modelers use to predict, for example, streamflow at some future time given knowledge of the current state of the system and model parameters. In this respect, good estimates of the parameters and state variables are needed to enable the model to generate accurate forecasts. In this paper, a dual state–parameter estimation approach is presented based on the Ensemble Kalman Filter (EnKF) for sequential estimation of both parameters and state variables of a hydrologic model. A systematic approach for identification of the perturbation factors used for ensemble generation and for selection of ensemble size is discussed. The dual EnKF methodology introduces a number of novel features: (1) both model states and parameters can be estimated simultaneously; (2) the algorithm is recursive and therefore does not require storage of all past information, as is the case in the batch calibration procedures; and (3) the various sources of uncertainties can be properly addressed, including input, output, and parameter uncertainties. The applicability and usefulness of the dual EnKF approach for ensemble streamflow forecasting is demonstrated using a conceptual rainfall-runoff model. 2004 Elsevier Ltd. All rights reserved.