Subjects -> MATHEMATICS (Total: 1013 journals)
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    - PROBABILITIES AND MATH STATISTICS (113 journals)

PROBABILITIES AND MATH STATISTICS (113 journals)                     

Showing 1 - 98 of 98 Journals sorted alphabetically
Advances in Statistics     Open Access   (Followers: 9)
Afrika Statistika     Open Access   (Followers: 1)
American Journal of Applied Mathematics and Statistics     Open Access   (Followers: 11)
American Journal of Mathematics and Statistics     Open Access   (Followers: 9)
Annals of Data Science     Hybrid Journal   (Followers: 14)
Annual Review of Statistics and Its Application     Full-text available via subscription   (Followers: 7)
Applied Medical Informatics     Open Access   (Followers: 11)
Asian Journal of Mathematics & Statistics     Open Access   (Followers: 8)
Asian Journal of Probability and Statistics     Open Access  
Austrian Journal of Statistics     Open Access   (Followers: 4)
Biostatistics & Epidemiology     Hybrid Journal   (Followers: 4)
Cadernos do IME : Série Estatística     Open Access  
Calcutta Statistical Association Bulletin     Hybrid Journal  
Communications in Mathematics and Statistics     Hybrid Journal   (Followers: 4)
Communications in Statistics - Simulation and Computation     Hybrid Journal   (Followers: 9)
Communications in Statistics: Case Studies, Data Analysis and Applications     Hybrid Journal  
Comunicaciones en Estadística     Open Access  
Econometrics and Statistics     Hybrid Journal   (Followers: 1)
Forecasting     Open Access   (Followers: 1)
Foundations and Trends® in Optimization     Full-text available via subscription   (Followers: 3)
Frontiers in Applied Mathematics and Statistics     Open Access   (Followers: 1)
Game Theory     Open Access   (Followers: 2)
Geoinformatics & Geostatistics     Hybrid Journal   (Followers: 14)
Geomatics, Natural Hazards and Risk     Open Access   (Followers: 13)
Indonesian Journal of Applied Statistics     Open Access  
International Game Theory Review     Hybrid Journal   (Followers: 1)
International Journal of Advanced Statistics and IT&C for Economics and Life Sciences     Open Access  
International Journal of Advanced Statistics and Probability     Open Access   (Followers: 7)
International Journal of Algebra and Statistics     Open Access   (Followers: 3)
International Journal of Applied Mathematics and Statistics     Full-text available via subscription   (Followers: 3)
International Journal of Ecological Economics and Statistics     Full-text available via subscription   (Followers: 4)
International Journal of Energy and Statistics     Hybrid Journal   (Followers: 3)
International Journal of Game Theory     Hybrid Journal   (Followers: 3)
International Journal of Mathematics and Statistics     Full-text available via subscription   (Followers: 2)
International Journal of Multivariate Data Analysis     Hybrid Journal  
International Journal of Probability and Statistics     Open Access   (Followers: 4)
International Journal of Statistics & Economics     Full-text available via subscription   (Followers: 6)
International Journal of Statistics and Applications     Open Access   (Followers: 2)
International Journal of Statistics and Probability     Open Access   (Followers: 3)
International Journal of Statistics in Medical Research     Hybrid Journal   (Followers: 5)
International Journal of Testing     Hybrid Journal   (Followers: 1)
Iraqi Journal of Statistical Sciences     Open Access  
Japanese Journal of Statistics and Data Science     Hybrid Journal  
Journal of Biometrics & Biostatistics     Open Access   (Followers: 4)
Journal of Cost Analysis and Parametrics     Hybrid Journal   (Followers: 5)
Journal of Environmental Statistics     Open Access   (Followers: 4)
Journal of Game Theory     Open Access   (Followers: 1)
Journal of Mathematical Economics and Finance     Full-text available via subscription  
Journal of Mathematics and Statistics Studies     Open Access  
Journal of Modern Applied Statistical Methods     Open Access   (Followers: 1)
Journal of Official Statistics     Open Access   (Followers: 2)
Journal of Quantitative Economics     Hybrid Journal  
Journal of Social and Economic Statistics     Open Access  
Journal of Statistical Theory and Practice     Hybrid Journal   (Followers: 2)
Journal of Statistics and Data Science Education     Open Access   (Followers: 2)
Journal of Survey Statistics and Methodology     Hybrid Journal   (Followers: 4)
Journal of the Indian Society for Probability and Statistics     Full-text available via subscription  
Jurnal Biometrika dan Kependudukan     Open Access  
Jurnal Ekonomi Kuantitatif Terapan     Open Access  
Jurnal Sains Matematika dan Statistika     Open Access  
Lietuvos Statistikos Darbai     Open Access  
Mathematics and Statistics     Open Access   (Followers: 2)
Methods, Data, Analyses     Open Access   (Followers: 1)
METRON     Hybrid Journal   (Followers: 1)
Nepalese Journal of Statistics     Open Access  
North American Actuarial Journal     Hybrid Journal   (Followers: 1)
Open Journal of Statistics     Open Access   (Followers: 3)
Open Mathematics, Statistics and Probability Journal     Open Access  
Pakistan Journal of Statistics and Operation Research     Open Access   (Followers: 1)
Physica A: Statistical Mechanics and its Applications     Hybrid Journal   (Followers: 6)
Probability, Uncertainty and Quantitative Risk     Open Access   (Followers: 2)
Ratio Mathematica     Open Access  
Research & Reviews : Journal of Statistics     Open Access   (Followers: 3)
Revista Brasileira de Biometria     Open Access  
Revista Colombiana de Estadística     Open Access  
RMS : Research in Mathematics & Statistics     Open Access  
Romanian Statistical Review     Open Access  
Sankhya B - Applied and Interdisciplinary Statistics     Hybrid Journal  
SIAM Journal on Mathematics of Data Science     Hybrid Journal   (Followers: 1)
SIAM/ASA Journal on Uncertainty Quantification     Hybrid Journal   (Followers: 2)
Spatial Statistics     Hybrid Journal   (Followers: 2)
Sri Lankan Journal of Applied Statistics     Open Access  
Stat     Hybrid Journal   (Followers: 1)
Stata Journal     Full-text available via subscription   (Followers: 8)
Statistica     Open Access   (Followers: 6)
Statistical Analysis and Data Mining     Hybrid Journal   (Followers: 23)
Statistical Theory and Related Fields     Hybrid Journal  
Statistics and Public Policy     Open Access   (Followers: 4)
Statistics in Transition New Series : An International Journal of the Polish Statistical Association     Open Access  
Statistics Research Letters     Open Access   (Followers: 1)
Statistics, Optimization & Information Computing     Open Access   (Followers: 3)
Stats     Open Access  
Synthesis Lectures on Mathematics and Statistics     Full-text available via subscription   (Followers: 1)
Theory of Probability and its Applications     Hybrid Journal   (Followers: 2)
Theory of Probability and Mathematical Statistics     Full-text available via subscription   (Followers: 2)
Turkish Journal of Forecasting     Open Access   (Followers: 1)
VARIANSI : Journal of Statistics and Its application on Teaching and Research     Open Access  
Zeitschrift für die gesamte Versicherungswissenschaft     Hybrid Journal  

           

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Journal Cover
Sankhya B - Applied and Interdisciplinary Statistics
Journal Prestige (SJR): 0.1
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0976-8386 - ISSN (Online) 0976-8394
Published by Springer-Verlag Homepage  [2469 journals]
  • Directional Measure for Analyzing the Degree of Deviance from Generalized
           Marginal Mean Equality Model in Square Contingency Tables

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      Abstract: Abstract When the concerned model does not fit the data, we may be interested in measuring the degree of deviance from the concerned model. This study proposes a measure for simultaneously analyzing the degree and direction of deviance from the generalized marginal mean equality model based on the ordered scores for each category. Previous study proposed a measure for analyzing both the degree and direction of deviance from the marginal mean equality model based on only the equally spaced scores. When it is appropriate to assign the ordered scores to categories, we are interested in analyzing whether the row marginal mean based on the known ordered scores is equal to the column marginal mean. It is necessary to analyze both the degree and direction of deviance from the generalized marginal mean equality model because there are two kinds of direction. We derive a confidence interval for the proposed measure using the delta method. The proposed measure is also helpful for comparing degrees of deviance from the generalized marginal mean equality model for several datasets. We show the utility of the proposed measure by applied it to real data.
      PubDate: 2022-05-12
       
  • Robust Moderately Clipped LASSO for Simultaneous Outlier Detection and
           Variable Selection

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      Abstract: Abstract Outlier detection has become an important and challenging issue in high-dimensional data analysis due to the coexistence of data contamination and high-dimensionality. Most existing widely used penalized least squares methods are sensitive to outliers due to the l2 loss. In this paper, we proposed a Robust Moderately Clipped LASSO (RMCL) estimator, that performs simultaneous outlier detection, variable selection and robust estimation. The RMCL estimator can be efficiently solved using the coordinate descent algorithm in a convex-concave procedure. Our numerical studies demonstrate that the RMCL estimator possesses superiority in both variable selection and outlier detection and thus can be advantageous in difficult prediction problems with data contamination.
      PubDate: 2022-05-11
       
  • Dynamic Copulas for Monotonic Dependence Change in Time Series

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      Abstract: Abstract A particular class of dynamic bivariate copulas, monotonically increasing or decreasing, is studied for modeling dependence in a time series. As increasing or decreasing functions of time, the copula parameters are estimated via their own parameters. The method of Inference Functions for Margins (IFM), adapted from the static case, is applied for this purpose. Simulations are used to assess the detectability of an increase or a decrease in dependence over time for five copula functions. In an application to wheat prices (source: Food and Agriculture Organization), information criteria are used to select the best copula function, and the dynamic copulas are shown to represent an improvement over static copulas for several of the time series.
      PubDate: 2022-05-11
       
  • Correction to: Novel Log Type Class of Estimators Under Ranked Set
           Sampling

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      Abstract: A Correction to this paper has been published: https://doi.org/10.1007/s13571-021-00270-1
      PubDate: 2022-05-01
       
  • Sequential Estimation of an Inverse Gaussian Mean with Known Coefficient
           of Variation

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      Abstract: Abstract This paper deals with developing sequential procedures for estimating the mean of an inverse Gaussian (IG) distribution when the population coefficient of variation (CV) is known. We consider the minimum risk and bounded risk point estimation problems respectively. Moreover, we make use of a weighted squared-error loss function and aim to control the associated risk functions. Instead of the usual estimator, i.e., the sample mean, Searls J. Amer. Stat. Assoc. 50, 1225–1226 (1964) estimator is utilized for the purpose of estimation. Second-order approximations are also obtained under both estimation set-ups. We establish that Searls’ estimator dominates the usual estimator (sample mean) under proposed sequential sampling procedures. An extensive simulation analysis is carried out to validate the theoretical findings and real data illustrations are also provided to show the practical utility of our proposed sequential stopping strategies.
      PubDate: 2022-05-01
       
  • Estimation and Influence Diagnostics for the Multivariate Linear
           Regression Models with Skew Scale Mixtures of Normal Distributions

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      Abstract: Abstract In this paper, we present recent results in the context of multivariate linear regression models considering that random errors follow multivariate skew scale mixtures of normal distributions. This class of distributions includes the scale mixtures of multivariate normal distributions, as special cases, and provides flexibility in capturing a wide variety of asymmetric behaviors. We implemented the algorithm ECM (Expectation/Conditional Maximization) and we obtained closed-form expressions for all the estimators of the parameters of the proposed model. Inspired by the ECM algorithm, we have developed an influence diagnostics for detecting influential observations to investigate the sensitivity of the maximum likelihood estimators. To examine the performance and the usefulness of the proposed methodology, we present simulation studies and analyze a real dataset.
      PubDate: 2022-05-01
       
  • A New Goodness-of-Fit Test for the Logistic Distribution

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      Abstract: Abstract The logistic distribution has been widely used to model growth curves in survival analysis and biological studies. In this article, we propose a goodness of fit test for the logistic distribution based on a new estimate of Kullback-Leibler information. The properties of the test statistic are presented. In order to compute the proposed test statistic, parameters of the logistic distribution are estimated by approximate maximum likelihood estimators (AMLEs) suggested by Balakrishnan and Cohen (1990), which are simple explicit estimators. Through a simulation study, power values of the proposed test are compared with some prominent existing tests. Finally, an illustrative example is presented and analyzed.
      PubDate: 2022-05-01
       
  • Testing the Validity of a Link Function Assumption in Repeated Type-II
           Censored General Step-Stress Experiments

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      Abstract: Abstract In step-stress experiments, test units are successively exposed to higher usually increasing levels of stress to cause earlier failures and to shorten the duration of the experiment. When parameters are associated with the stress levels, one problem is to estimate the parameter corresponding to normal operating conditions based on failure data obtained under higher stress levels. For this purpose, a link function connecting parameters and stress levels is usually assumed, the validity of which is often at the discretion of the experimenter. In a general step-stress model based on multiple samples of sequential order statistics, we provide exact statistical tests to decide whether the assumption of some link function is adequate. The null hypothesis of a proportional, linear, power or log-linear link function is considered in detail, and associated inferential results are stated. In any case, except for the linear link function, the test statistics derived are shown to have only one distribution under the null hypothesis, which simplifies the computation of (exact) critical values. Asymptotic results are addressed, and a power study is performed for testing on a log-linear link function. Some improvements of the tests in terms of power are discussed.
      PubDate: 2022-05-01
       
  • Bayesian Hierarchical Modeling: Application Towards Production Results in
           the Eagle Ford Shale of South Texas

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      Abstract: Abstract Recently, the petroleum industry has faced the era of data explosion, and many oil and gas companies resort to data-driven approaches for unconventional field development planning. The objective of this paper is to analyze shale oil wells in a shale reservoir and develop a statistical model useful for upstream. Shale oil wells dataset comprises three aspects of information: oil production rate time series data; well completion data; and well location data. However, traditional decline curve analysis only utilizes the temporal trajectory of the production rates. Motivated by this, we propose a Bayesian hierarchical model that exploits the full aspects of the shale oil wells data. The proposed model provides the following three functionalities: first, estimations of a production decline curve at an individual well and entire reservoir levels; second, identification of significant completion predictors explaining a well productivity; and third, spatial predictions for the oil production rate trajectory of a new well provided completion predictors. As a fully Bayesian approach has been adopted, the functionalities are endowed with uncertainty quantification which is a crucial task in investigating unconventional reservoirs. The data for this study come from 360 shale oil wells completed in the Eagle Ford Shale of South Texas.
      PubDate: 2022-05-01
       
  • The Power Series Exponential Power Series Distributions with Applications
           to Failure Data Sets

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      Abstract: Abstract A new class of distributions motivated by systems having both series and parallel structures is introduced. Some mathematical properties of the new class (including the moment generating function, moments and order statistics) are derived. Estimation is addressed by the maximum likelihood method and the performance of the estimators assessed by a simulation study. An illustration using three failure data sets shows the usefulness of the new class.
      PubDate: 2022-05-01
       
  • An Investigation into Adult Human Height Distributions Using Kernel
           Density Estimation

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      Abstract: Abstract This study investigates how average adult human height distributions of various regions around the world have changed over time using a non-parametric approach. Performance of kernel density estimators (KDEs) were compared between various mixtures of Gaussian distributions created using different means, variances and mixing weights. The performance was evaluated for these mixtures using existing bandwidth selection methods, with various kernels and sample sizes and it was revealed for mixtures with distinct multi modes the Sheather & Jones method performed better in general among the considered. The results of this study also revealed that a better practical performance than Sheather & Jones can be achieved for relatively smaller samples from gaussian mixtures in general through a modified plug-in bandwidth. By applying the findings of the simulation analysis on data related to average adult human heights in different regions in the world for different cohorts, interesting observations on average adult human height distributions were made.
      PubDate: 2022-05-01
       
  • Convergence Details About k-DPP Monte-Carlo Sampling for Large Graphs

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      Abstract: Abstract This paper aims at making explicit the mixing time found by Anari et al. (2016) for k-DPP Monte-Carlo sampling when it is applied on large graphs. This yields a polynomial bound on the mixing time of the associated Markov chain under mild conditions on the eigenvalues of the Laplacian matrix when the number of edges grows.
      PubDate: 2022-05-01
       
  • Fixed versus Mixed Effects Based Marginal Models for Clustered Correlated
           Binary Data: an Overview on Advances and Challenges

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      Abstract: Abstract In a cross-sectional cluster setup, the binary responses from the individuals in a cluster become correlated as they share a common cluster effect, whereas longitudinal responses from an individual those form a cluster become correlated as the present and past responses are likely to maintain a suitable dynamic relationship. In both cluster and longitudinal setups, the marginal means may or may not be specified as the function of regression effects/parameters only. In a cluster setup, this depends on the distributional assumption of the random cluster effects and in a longitudinal setup this depends on the form such as linear or non-linear dynamic relationships used to construct a conditional model. However, over the last four decades, many studies arbitrarily pre-specified the marginal means as the function of regression effects only under both cluster and longitudinal setups and accommodated correlations also using arbitrarily selected ‘working’ correlation structures. This paper makes a thorough in-depth review of these decades long binary correlation models for consistent and efficient estimation of the regression effects. Both progress and drawbacks of these works are presented clearly showing how the inconsistency can arise if the pre-specified marginal fixed model is used when in fact such a marginal fixed effects model does not exist. This is because, some of the conditional random effects models in a cluster setup produce mixed effect models for the marginal means, and conditional non-linear dynamic models in a longitudinal setup produce history based marginal recursive/dynamic models. As the practitioners in both cluster and longitudinal setups deal with large data sets, it is demonstrated for their benefits how one can use the GQL (generalized quasi-likelihood) estimation approach both in cluster and longitudinal setups. Furthermore, there exist many studies using the Bayesisn approach where unlike the aforementioned parametric correlation structure based inferences, the marginal mixed effects models have been used for inferences for correlated binary data without specifying their correlation structures, under both cluster and longitudinal setup. We also provide a brief review on this alternative approach.
      PubDate: 2022-05-01
       
  • Novel Log Type Class Of Estimators Under Ranked Set Sampling

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      Abstract: Abstract This paper suggests some novel class of log type estimators for the estimation of population mean of study variable under ranked set sampling by utilizing information on population mean of auxiliary variable. The mean square error of the proposed class of estimators is obtained to the first order of approximation. We have compared the proposed class of estimators with some existing competitors under some specific conditions. The theoretical results are validated by a computational study using real and simulated data sets. On the lines of McIntyre (Aust. J. Agr. Res. 3, 385–390 1952), Dell (1969) and Dell and Clutter (Biometrics 28, 545–555 1972), the effect of skewness and kurtosis over the efficiency of the proposed class of estimators have also studied and reported.
      PubDate: 2022-05-01
       
  • Reliability Estimation in a Multicomponent Stress-Strength Model Based on
           Inverse Weibull Distribution

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      Abstract: Abstract Reliability inference in a multicomponent stress-strength (MSS) model is studied when components are exposed to a specific random stress. Stress and strength variables are assumed to follow inverse Weibull distributions with different scale and same shape parameter. A s-out-of-k:G system fails if s or more components simultaneously become inoperative. Different estimates of MSS reliability are obtained from frequentist and Bayesian viewpoint. In particular Bayes estimates are evaluated from Lindley method and Metropolis-Hastings algorithm. Unbiased estimation is also considered when shape parameter is known. We construct asymptotic intervals and obtain corresponding coverage probabilities using observed information matrix. In sequel credible intervals are also obtained. A simulation study is performed to examine the estimated risks of proposed estimation methods and analyze two numerical examples from application viewpoint. Finally, optimal plans are discussed for the multicomponent system.
      PubDate: 2022-05-01
       
  • Economic Statistical Design for Three-level Control Charts with Variable
           Sample Size

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      Abstract: Abstract The problem of Statistical Process Control with some discrete level classification scheme, have been recently discussed by some authors. This paper presents economic and economic-statistical designs for variable sample size (VSS) of the three-level control charts. The expected cost per unit of time is derived with the use of the cost model developed for the control chart proposed by Costa and Rahim. The genetic algorithm is employed to search for the optimal values of model parameters. Finally, the expected cost per unit of time of VSS and fixed ratio sampling (FRS) three-level control charts is compared.
      PubDate: 2022-05-01
       
  • The General Tail Dependence Function in the Marshall-Olkin and Other
           Parametric Copula Models with an Application to Financial Time Series

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      Abstract: Abstract The accurate understanding of the dependence structure implied by the parametric models studied in statistical and financial literature has drawn growing attention in recent times. In particular, tail dependence is crucial in this analysis. We study the general tail dependence function in some of the most common copula models found in literature, in which this function has not been obtained. These models are used by statisticians and practitioners alike. With the general tail dependence function, we cover positive and non-positive tail dependence often overlooked. The use of the general tail dependence function generalises the well known approach of using the survival copula to tackle upper tail dependence. We present relevant results regarding tail dependence related functions. We include a broad guide for bivariate families and Hierarchical Archimedean copulas in dimensions 3 and 4. In the multivariate case we study the Marshall-Olkin copula, examples of models based on Max-Id distributions and Extreme Value copulas among others. In an empirical section we exemplify with real data the usefulness of our results by modelling arbitrary types of tail dependence. Furthermore we show how our approach can yield better estimates of Value at Risk than other standard approaches.
      PubDate: 2022-05-01
       
  • An Unbiased Regression Type Estimator In Randomized Response Sampling

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      Abstract: Abstract In this paper, we suggest a new method of constructing an unbiased regression type estimator in randomized response sampling. We introduce two new randomized response estimators, one we created through the utilization of a sum of special products technique and the other through the utilization of the method used for computing a matrix determinant. This new idea of making an unbiased regression type estimator proves to be more efficient with no loss in respondent protection. Analytical comparisons show the proposed unbiased regression type estimator is always more efficient than the considered competitors. The theoretical justification that the proposed estimator has a smaller variance over its competitors is crystal clear, so no simulation study is required. However to study the gain in magnitude of the relative efficiency, a simulation study has been carried out.
      PubDate: 2022-05-01
       
  • The Sibling Distribution for Multivariate Life Time Data

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      Abstract: Abstract A flexible class of multivariate distributions for continuous lifetimes is proposed. The distribution is defined in terms of the age-at-death of m siblings. The expression for the joint density is derived using classical results from mathematical demography. The parameters of the distribution are the age-specific birth and death rates, in addition to a vector of relative death times for the m siblings. For the case of constant birth and death rates we are able to derive an explicit expression for the bivariate sibling density, which is proven to be MTP2, and hence has positive dependence. Further, we show that a special case of the sibling distribution belongs to the Block-Basu class of multivariate distribution. In the general case, with age-dependent birth and death rates, evaluation of the density involves numerical integration, but is still feasible.
      PubDate: 2022-05-01
       
  • A Curtailed Procedure for Selecting Among Treatments With Two Bernoulli
           Endpoints

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      Abstract: Abstract This paper is concerned with a closed adaptive sequential procedure for selecting a random-size subset containing experimental treatments that are better than a standard. All the k treatments under considerations are measured by two endpoints accounting for treatment efficacy and treatment safety respectively. The selection is made with regard to the two binary endpoints. An experimental treatment is considered to be better than the standard if its both endpoints have successful rates higher than the standard ones. We provide a step-by-step sampling rule, stopping rule, and decision rule for the proposed procedure. We show that the proposed sequential procedure achieves the same requirements for the probability of a correct selection as does the fixed-sample-size procedure, but requires fewer observations. We use simulations to evaluate the sample size savings of the proposed procedure over the corresponding fixed-sample-size procedure.
      PubDate: 2022-05-01
       
 
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