Showing papers in "Sequential Analysis in 2019"
TL;DR: In some cases, a specific count, usually zero count as mentioned in this paper, is observed in several applied disciplines such as environmental science, biostatistics, economics, public health, and finance.
Abstract: Count time series data are observed in several applied disciplines such as environmental science, biostatistics, economics, public health, and finance. In some cases, a specific count, usually zero...
16 citations
TL;DR: Adaptive designs play an important role in contemporary clinical trials to make designs flexible and efficient as discussed by the authors, and given a relatively small sample size, it is important to give adaptive designs in cancer clinical trials.
Abstract: Adaptive designs play an important role in contemporary clinical trials to make designs flexible and efficient. In cancer clinical trials, given a relatively small sample size, it is important to o...
13 citations
TL;DR: In this paper, the authors developed two-stage, three-stage and accelerated sequential procedures for the point estimation of the mean μ of an inverse Gaussian distribution when the scale parameter λ is unknown.
Abstract: Abstract In this article, we develop two-stage, three-stage, and accelerated sequential procedures for the point estimation of the mean μ of an inverse Gaussian distribution when the scale parameter λ is unknown. Both minimum risk and bounded risk estimation problems are considered subject to a weighted squared error loss function. We aim at controlling the associated risk functions for all three procedures. Second-order approximations are obtained for the proposed procedures.
11 citations
TL;DR: In this paper, the authors developed appropriate sampling methodologies for testing hypotheses regarding the difference of mean values from two independent (or dependent) normal populations when their variances were different.
Abstract: In this paper, we develop appropriate sampling methodologies for testing hypotheses regarding the difference of mean values from two independent (or dependent) normal populations when their varianc...
9 citations
TL;DR: In this paper, a purely sequential minimum risk point estimation (MRPE) methodology with associated stopping time N is designed to come up with a useful estimation strategy, under an appropriately formula.
Abstract: A purely sequential minimum risk point estimation (MRPE) methodology with associated stopping time N is designed to come up with a useful estimation strategy. We work under an appropriately formula...
8 citations
TL;DR: This paper derives exact critical values for CMaxSPRT, as well as statistical power and expected time to signal, for both continuous and group sequential analysis, and for different rejection boundaries.
Abstract: Sequential analysis is now commonly used for post-market drug and vaccine safety surveillance, and a Poisson stochastic process is typically used for rare adverse events. The conditional maximized ...
6 citations
TL;DR: A new adaptive sequential model selection method based on the efficient sequential kernel estimators proposed by Arkoun and Pergamenshchikov (2016) and a new analytical tool for general regression models to obtain the non-asymptotic sharp oracle inequalities for both usual Quadratic and robust quadratic risks.
Abstract: In this paper for the first time the nonparametric autoregression estimation problem for the quadratic risks is considered. To this end we develop a new adaptive sequential model selection method based on the efficient sequential kernel estimators proposed by Arkoun and Pergamenshchikov (2016). Moreover, we develop a new analytical tool for general regression models to obtain the non asymptotic sharp oracle inequalities for both usual quadratic and robust quadratic risks. Then, we show that the constructed sequential model selection procedure is optimal in the sense of oracle inequalities. MSC: primary 62G08, secondary 62G05
6 citations
TL;DR: A bound is developed to show that the estimate of the change in the minimizers is non trivial provided that the excess risk is small enough and the expected gap between the loss achieved by the approximate minimizer produced by the optimization algorithm and the exact minimizer does not exceed a target level.
Abstract: A framework previously introduced in Wilson et al. (2018) for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learni...
5 citations
TL;DR: In this paper, the problem of comparing several Bernoulli populations in order to identify the population producing the largest success probability that is also larger than a g is considered, and the problem is solved by comparing the probability of each population to a Gaussian distribution.
Abstract: In this article we consider the problem of comparing several Bernoulli populations in order to identify the population producing the largest success probability that is also larger than a g...
4 citations
TL;DR: In this paper, a goodness-of-fit test for the distribution function or the survival function in a recurrent event setting is provided, when the inter-event time parametric structure F(·;θ) is estimated.
Abstract: This article provides a goodness-of-fit test for the distribution function or the survival function in a recurrent event setting, when the inter-event time parametric structure F(·;θ) is estimated ...
4 citations
TL;DR: In this paper, the authors consider a Poisson process where the arrival rates change from a known λ 1 to a unknown λ 2 and show that the change point is not abrupt.
Abstract: We look at a Poisson process where the arrival rates change from a known λ1 to a known λ2. Whereas in most of the literature the change-point is abrupt, we model the more realistic assumption that ...
TL;DR: In this paper, two-stage and fixed sample size procedures are developed for constructing confidence intervals for the common correlation ρ of an equi-correlated multivariate normal distribution.
Abstract: In this article, two-stage and fixed sample size procedures are developed for constructing confidence intervals for the common correlation ρ of an equi-correlated multivariate normal distribution
TL;DR: In this article, a two-stage sequential method for obtaining tandem-width confidence intervals for a Bernoulli proportion p was proposed, where the term tandemwidth refers to the fact that the halfwidth of the 100(1−α)
Abstract: We propose a two-stage sequential method for obtaining tandem-width confidence intervals for a Bernoulli proportion p. The term “tandem-width” refers to the fact that the half-width of the 100(1−α)...
TL;DR: A sequential procedure is proposed that promises a significant gain in the sample size thus reduction in the costs of implementation and risk efficient in the sense that as the cost of sampling becomes negligible the asymptotic predictive risk of the proposed procedure reaches the oracle predictive risk corresponding to the best fixed sample size procedure.
Abstract: A vector-valued autoregressive time series model is considered. The autoregressive coefficients of the model are random with possible dependencies among them. Estimation of the large number of para...
TL;DR: In this paper, the mean vector of a multivariate normal population was estimated by using a k-stage sequential estimation procedure, and second-order approximations were obtained in both the cases.
Abstract: Abstract In this article, we have estimated the mean vector of a multivariate normal population by using a k-stage sequential estimation procedure. Point estimation as well as confidence region estimation is done. Second-order approximations are obtained in both the cases. In case of minimum risk point estimation of , negative regret is achieved.
TL;DR: In this paper, the authors revisited the problem of sequential point estimation of the autogressive parameter in an autoregressive model of order 1, where the errors are independent and identically distributed.
Abstract: This article revisits the problem of sequential point estimation of the autogressive parameter in an autoregressive model of order 1, where the errors are independent and identically distributed wi...
TL;DR: In this article, a zero-mean and second-order stationary time series of interest (Xt) that cannot be observed directly is considered and an amplitude-modulated time series (Yt) is observed where Xt is a
Abstract: Consider a zero-mean and second-order stationary time series of interest {Xt} that cannot be observed directly Instead an amplitude-modulated time series {Yt}:={AtUtXt} is observed where {At} is a
TL;DR: In this article, the authors deal with construction of fixed accuracy confidence intervals for the common variance in compound symmetric multivariate normal distribution by adopting a Stein-type two-stage setup and a completely sequential setup.
Abstract: Abstract This work deals with construction of fixed accuracy confidence intervals for the common variance in compound symmetric multivariate normal distribution by adopting a Stein-type two-stage setup and a completely sequential setup. Related asymptotics are developed and the procedures are evaluated by simulation study.
TL;DR: The article paper shows how to reduce the full MES method to a simple one by using a special empirical Bayes approximation, rather than using time-consuming integration.
Abstract: Maximum entropy sampling (MES) criteria provide a useful framework for studying sequential designs for computer experiments in a Bayesian framework. However, there is some technical difficulty in making the procedure fully adaptive in the sense of making proper use of previous output as well as input data. In the simple Gaussian set-up only previous input values need to be used. The approach discussed uses a full hierarchical model for the Gaussian process. The idea is to take advantage of the Karhumen-Loeve (K-L) expansion to approximate the process covariance function using an orthogonal function basis. It is argued that this may make it easier to use Bayesian hierarchical models, rather than estimating the covariance parameters directly, using the traditional approach. The article paper shows how to reduce the full MES method to a simple one by using a special empirical Bayes approximation, rather than using time-consuming integration. A simple simulator example is presented to show that full adaptation is beneficial.
TL;DR: This article used kernel-based nonparametric statistics to detect abrupt change-point detection in statistics and machine learning models, which enjoys fewer assents than the traditional stochastic model.
Abstract: Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning. Kernel-based nonparametric statistics have been used for this task, which enjoys fewer ass...
TL;DR: In this article, the problem of minimum risk point estimation of the reliability parameter (R) under the stress-strength model, in case the loss is small, was studied. But the reliability parameters were not considered in this paper.
Abstract: In this article, using purely and two-stage sequential procedures, the problem of minimum risk point estimation of the reliability parameter (R) under the stress–strength model, in case the loss fu...
TL;DR: In this paper, the problem of detecting a drift change of a Brownian motion under various extensions of the classical case was studied, and the authors considered the case of a random post-change drift.
Abstract: We study the problem of detecting a drift change of a Brownian motion under various extensions of the classical case. Specifically, we consider the case of a random post-change drift and examine mo...
TL;DR: In this article, a sequential minimum risk point estimation (MRPE) strategy for the unknown mean of a normal population having its variance unknown too was designed under a Linex loss plus a linex loss.
Abstract: We have designed a sequential minimum risk point estimation (MRPE) strategy for the unknown mean of a normal population having its variance unknown too. This is developed under a Linex loss plus li...
TL;DR: In this article, a hybrid-Bayesian frequentist approach using a Bayesian sequential prediction of the index of satisfaction is proposed to solve the problem of finding the optimal index for each user.
Abstract: In this article, we propose a hybrid-Bayesian frequentist approach using a Bayesian sequential prediction of the index of satisfaction. For interim analysis that addresses prediction hypoth...
TL;DR: In this paper, a sampling plan called a multiple dependent state repetitive group sampling plan for variable inspection based on the process capability index Cpk is proposed, and the proposed sampling plan can be used for multiple dependent states.
Abstract: In this article, we propose a sampling plan called a multiple dependent state repetitive group sampling plan for variable inspection based on the process capability index Cpk. The proposed sampling...
TL;DR: In this article, the authors developed purely sequential, two-stage, three-stage and "accelerated" sequential procedures for the point estimation of the location of negative exponential distribution having an unknown scale parameter.
Abstract: Abstract In this paper, we develop purely sequential, two-stage, three-stage, and “accelerated” sequential procedures for the point estimation of the location of negative exponential distribution having an unknown scale parameter. We do minimum risk point estimation by using all four sequential procedures mentioned above. In addition, we do bounded risk point estimation by using three-stage and “accelerated” sequential procedures. Consideration is given to a modified LINEX loss function. We aim at controlling the associated risk functions for all the procedures. Second-order approximations are obtained for the proposed procedures. We also discuss first-order properties for purely sequential procedure.