Sequential Estimation
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Sequential Estimation
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Author : Malay Ghosh
language : en
Publisher: John Wiley & Sons
Release Date : 2011-09-09
Sequential Estimation written by Malay Ghosh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-09 with Mathematics categories.
The only comprehensive guide to the theory and practice of one oftoday's most important probabilistic techniques The past 15 years have witnessed many significant advances insequential estimation, especially in the areas of three-stage andnonparametric methodology. Yet, until now, there were no referencesdevoted exclusively to this rapidly growing statisticalfield. Sequential Estimation is the first, single-source guide to thetheory and practice of both classical and modern sequentialestimation techniques--including parametric and nonparametricmethods. Researchers in sequential analysis will appreciate theunified, logically integrated treatment of the subject, as well ascoverage of important contemporary procedures not covered in moregeneral sequential analysis texts, such as: * Shrinkage estimation * Empirical and hierarchical Bayes procedures * Multistage sampling and accelerated sampling procedures * Time-sequential estimation * Sequential estimation in finite population sampling * Reliability estimation and capture-recapture methodologiesleading to sequential tagging schemes An indispensable resource for researchers in sequential analysis,Sequential Estimation is an ideal graduate-level text as well.
Factorization Methods For Discrete Sequential Estimation
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Author : Gerald J. Bierman
language : en
Publisher: Courier Corporation
Release Date : 2006-05-26
Factorization Methods For Discrete Sequential Estimation written by Gerald J. Bierman and has been published by Courier Corporation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-26 with Mathematics categories.
This estimation reference text thoroughly describes matrix factorization methods successfully employed by numerical analysts, familiarizing readers with the techniques that lead to efficient, economical, reliable, and flexible estimation algorithms. Topics include a review of least squares data processing and the Kalman filter algorithm; positive definite matrices, the Cholesky decomposition, and some of their applications; Householder orthogonal transformations; sequential square root data processing; mapping effects and process noise; biases and correlated process noise; and covariance analysis of effects due to mismodeled variables and incorrect filter a priori statistics. The concluding chapters explore SRIF error analysis of effects due to mismodeled variables and incorrect filter a priori statistics as well as square root information smoothing. Geared toward advanced undergraduates and graduate students, this pragmatically oriented and detailed presentation is also a useful reference, featuring numerous helpful appendixes throughout the text.
Sequential Estimation Of The Mean Of A Normal Population
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Author : Herbert Robbins
language : en
Publisher:
Release Date : 1958
Sequential Estimation Of The Mean Of A Normal Population written by Herbert Robbins and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1958 with Estimation theory categories.
Sequential Statistical Procedures
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Author : Z. Govindarajulu
language : en
Publisher: Academic Press
Release Date : 2014-06-20
Sequential Statistical Procedures written by Z. Govindarajulu and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-20 with Mathematics categories.
Probability and Mathematical Statistics, Volume 26: Sequential Statistical Procedures provides information pertinent to the sequential procedures that are concerned with statistical analysis of data. This book discusses the fundamental aspects of sequential estimation. Organized into four chapters, this volume begins with an overview of the essential feature of sequential procedure. This text then examines the sequential probability ratio test procedure and provides a method of constructing a most powerful test for a simple hypothesis versus simple alternative-testing problem. Other chapters consider the problem of testing a composite hypothesis against a composite alternative. This book discusses as well the theory of sequential tests that is appropriate for distinguishing between two simple or composite hypotheses. The final chapter deals with the theory of sequential estimation. This book is a valuable resource for graduate students, research workers, and users of sequential procedures.
Sequential Estimation Of The Common Mean Of Two Normal Populations
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Author : Ashim Kumar Mallik
language : en
Publisher:
Release Date : 1971
Sequential Estimation Of The Common Mean Of Two Normal Populations written by Ashim Kumar Mallik and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with Sequential analysis categories.
Some Contributions To The Theory Of Sequential Estimation
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Author : R. Singh
language : en
Publisher:
Release Date : 1984
Some Contributions To The Theory Of Sequential Estimation written by R. Singh and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1984 with categories.
Sequential Methods And Their Applications
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Author : Nitis Mukhopadhyay
language : en
Publisher: CRC Press
Release Date : 2008-10-28
Sequential Methods And Their Applications written by Nitis Mukhopadhyay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-10-28 with Mathematics categories.
Interactively Run Simulations and Experiment with Real or Simulated Data to Make Sequential Analysis Come AliveTaking an accessible, nonmathematical approach to this field, Sequential Methods and Their Applications illustrates the efficiency of sequential methodologies when dealing with contemporary statistical challenges in many areas.The book fir
Sequential Estimation Of Parameters In A First Order Autoregressive Model
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Author : Tharuvai Narayanaswami Sriram
language : en
Publisher:
Release Date : 1986
Sequential Estimation Of Parameters In A First Order Autoregressive Model written by Tharuvai Narayanaswami Sriram and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986 with Autoregression (Statistics) categories.
Some Problems In Sequential Estimation
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Author : Yehuda Vardi
language : en
Publisher:
Release Date : 1977
Some Problems In Sequential Estimation written by Yehuda Vardi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1977 with Estimation theory categories.
Sequential Estimation In Statistics And Steady State Simulation
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Author : Peng Tang
language : en
Publisher:
Release Date : 2014
Sequential Estimation In Statistics And Steady State Simulation written by Peng Tang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Algorithms categories.
At the onset of the "Big Data" age, we are faced with ubiquitous data in various forms and with various characteristics, such as noise, high dimensionality, autocorrelation, and so on. The question of how to obtain accurate and computationally efficient estimates from such data is one that has stoked the interest of many researchers. This dissertation mainly concentrates on two general problem areas: inference for high-dimensional and noisy data, and estimation of the steady-state mean for univariate data generated by computer simulation experiments. We develop and evaluate three separate sequential algorithms for the two topics. One major advantage of sequential algorithms is that they allow for careful experimental adjustments as sampling proceeds. Unlike one-step sampling plans, sequential algorithms adapt to different situations arising from the ongoing sampling; this makes these procedures efficacious as problems become more complicated and more-delicate requirements need to be satisfied. We will elaborate on each research topic in the following discussion. Concerning the first topic, our goal is to develop a robust graphical model for noisy data in a high-dimensional setting. Under a Gaussian distributional assumption, the estimation of undirected Gaussian graphs is equivalent to the estimation of inverse covariance matrices. Particular interest has focused upon estimating a sparse inverse covariance matrix to reveal insight on the data as suggested by the principle of parsimony. For estimation with high-dimensional data, the influence of anomalous observations becomes severe as the dimensionality increases. To address this problem, we propose a robust estimation procedure for the Gaussian graphical model based on the Integrated Squared Error (ISE) criterion. The robustness result is obtained by using ISE as a nonparametric criterion for seeking the largest portion of the data that "matches" the model. Moreover, an l1-type regularization is applied to encourage sparse estimation. To address the non-convexity of the objective function, we develop a sequential algorithm in the spirit of a majorization-minimization scheme. We summarize the results of Monte Carlo experiments supporting the conclusion that our estimator of the inverse covariance matrix converges weakly (i.e., in probability) to the latter matrix as the sample size grows large. The performance of the proposed method is compared with that of several existing approaches through numerical simulations. We further demonstrate the strength of our method with applications in genetic network inference and financial portfolio optimization. The second topic consists of two parts, and both concern the computation of point and confidence interval (CI) estimators for the mean æ of a stationary discrete-time univariate stochastic process X \equiv \{X_i: i=1,2 ... } generated by a simulation experiment. The point estimation is relatively easy when the underlying system starts in steady state; but the traditional way of calculating CIs usually fails since the data encountered in simulation output are typically serially correlated. We propose two distinct sequential procedures that each yield a CI for æ with user-specified reliability and absolute or relative precision. The first sequential procedure is based on variance estimators computed from standardized time series applied to nonoverlapping batches of observations, and it is characterized by its simplicity relative to methods based on batch means and its ability to deliver CIs for the variance parameter of the output process (i.e., the sum of covariances at all lags). The second procedure is the first sequential algorithm that uses overlapping variance estimators to construct asymptotically valid CI estimators for the steady-state mean based on standardized time series. The advantage of this procedure is that compared with other popular procedures for steady-state simulation analysis, the second procedure yields significant reduction both in the variability of its CI estimator and in the sample size needed to satisfy the precision requirement. The effectiveness of both procedures is evaluated via comparisons with state-of-the-art methods based on batch means under a series of experimental settings: the M/M/1 waiting-time process with 90% traffic intensity; the M/H_2/1 waiting-time process with 80% traffic intensity; the M/M/1/LIFO waiting-time process with 80% traffic intensity; and an AR(1)-to-Pareto (ARTOP) process. We find that the new procedures perform comparatively well in terms of their average required sample sizes as well as the coverage and average half-length of their delivered CIs.