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In this article we propose a procedure for selecting a random size subset that contains all experimental treatments that are better than the standard. The comparison is made with regard to two binary endpoints. An experimental treatment is considered to be better than the standard if its two endpoints have successful rates higher than standard rates. We assume responses from different treatments are independent, however, responses from each treatment may be associated. We use the odds ratio as the association parameter and describe the bivariate binomial distribution involved in the computation of the probability of a correct selection for our procedure. We derive the design parameters for three different cases: independent endpoints, dependent endpoints with known association and dependent endpoints with unknown association, and provide tables including the minimum sample size needed for the probability requirements to be satisfied. [ABSTRACT FROM AUTHOR]

The operating characteristics (OCs) of a subset ranking and selection procedure are derived for a randomized response model for continuous data. In this article the operating characteristics for the subset selection procedure are considered for two parameter configurations, the slippage configuration and the equi-spaced configuration, and tables are provided for each case for some specific choices of problems parameters. [ABSTRACT FROM AUTHOR]

Dominance analysis is a procedure for measuring the importance of predictors in multiple regression analysis. We show that dominance analysis can be enhanced using a dynamic programing approach for the rank-ordering of predictors. Using customer satisfaction data from a call center operation, we demonstrate how the integration of dominance analysis with dynamic programing can provide a better understanding of predictor importance. As a cautionary note, we recommend careful reflection on the relationship between predictor importance and variable subset selection. We observed that slight changes in the selected predictor subset can have an impact on the importance rankings produced by a dominance analysis. [ABSTRACT FROM AUTHOR]

Subset selection is an extensively studied problem in statistical learning. Especially it becomes popular for regression analysis. This problem has considerable attention for generalized linear models as well as other types of regression methods. Quantile regression is one of the most used types of regression method. In this article, we consider subset selection problem for quantile regression analysis with adopting some recent Bayesian information criteria. We also utilized heuristic optimization during selection process. Simulation and real data application results demonstrate the capability of the mentioned information criteria. According to results, these information criteria can determine the true models effectively in quantile regression models. [ABSTRACT FROM PUBLISHER]

Considerk( ⩾ 2) normal populations whose means are all known or unknown and whose variances are unknown. Let σ2[1]⩽ ⋅⋅⋅ ⩽ σ[k]2denote the ordered variances. Our goal is to select a non empty subset of thekpopulations whose size is at mostm(1 ⩽m⩽k− 1) so that the population associated with the smallest variance (called the best population) is included in the selected subset with a guaranteed minimum probabilityP whenever σ2[2]/σ[1]2⩾ δ > 1, whereP and δ are specified in advance of the experiment. Based on samples of sizenfrom each of the populations, we propose and investigate a procedure calledRBCP. We also derive some asymptotic results for our procedure. Some comparisons with an earlier available procedure are presented in terms of the average subset sizes for selected slippage configurations based on simulations. The results are illustrated by an example. [ABSTRACT FROM AUTHOR]

In this paper, we translate variable selection for linear regression into multiple testing, and select significant variables according to testing result. New variable selection procedures are proposed based on the optimal discovery procedure (ODP) in multiple testing. Due to ODP’s optimality, if we guarantee the number of significant variables included, it will include less non significant variables than marginalp-value based methods. Consistency of our procedures is obtained in theory and simulation. Simulation results suggest that procedures based on multiple testing have improvement over procedures based on selection criteria, and our new procedures have better performance than marginalp-value based procedures. [ABSTRACT FROM AUTHOR]

The operating characteristics (OCs) of a subset ranking and selection procedure are derived for the hybrid randomized response model developed by Jia and McDonald (2009). The OCs include the probability of a correctP(CS), the individual selection probability γi, and the expected subset sizeE(S), under the slippage configuration or the equi-spaced configuration. An example comparing failure rates of contraceptive methods is used to illustrate the use of these new results. [ABSTRACT FROM PUBLISHER]

Considerk( ⩾ 2) normal populations with unknown means μ1, …, μk, and a common known variance σ2. Let μ[1]⩽ ⋅⋅⋅ ⩽ μ[k]denote the ordered μi.The populations associated with thet(1 ⩽t⩽k− 1) largest means are called the t best populations. Hsu and Panchapakesan (2004) proposed and investigated a procedureRHPfor selecting a non empty subset of the k populations whose size is at mostm(1 ⩽m⩽k−t) so that at least one of the t best populations is included in the selected subset with a minimum guaranteed probability P* whenever μ[k − t + 1]− μ[k − t]⩾ δ*, whereP* and δ* are specified in advance of the experiment. This probability requirement is known as the indifference-zone probability requirement. In the present article, we investigate the same procedureRHPfor the same goal as before but whenk−t

Two statistical applications for estimation and prediction of flows in traffic networks are presented. In the first, the number of route users are assumed to be independent α-shifted gamma Γ(θ, λ0) random variables denotedH(α, θ, λ0), with common λ0. As a consequence, the link, OD (origin-destination) and node flows are alsoH(α, θ, λ0) variables. We assume that the main source of information is plate scanning, which permits us to identify, totally or partially, the vehicle route, OD and link flows by scanning their corresponding plate numbers at an adequately selected subset of links. A Bayesian approach using conjugate families is proposed that allows us to estimate different traffic flows. In the second application, a stochastic demand dynamic traffic model to predict some traffic variables and their time evolution in real networks is presented. The Bayesian network model considers that the variables are generalized Beta variables such that when marginally transformed to standard normal become multivariate normal. The model is able to provide a point estimate, a confidence interval or the density of the variable being predicted. Finally, the models are illustrated by their application to the Nguyen Dupuis network and the Vermont-State example. The resulting traffic predictions seem to be promising for real traffic networks and can be done in real time. [ABSTRACT FROM AUTHOR]

Determination of the best subset is an important step in vector autoregressive (VAR) modeling. Traditional methods either conduct subset selection and parameter estimation separately or compute expensively. In this article, we propose a VAR model selection procedure using adaptive Lasso, for it is computational efficient and can select subset and estimate parameters simultaneously. By proper choice of tuning parameters, we can choose the correct subset and obtain the asymptotic normality of the non zero parameters. Simulation studies and real data analysis show that adaptive Lasso performs better than existing methods in VAR model fitting and prediction. [ABSTRACT FROM PUBLISHER]

Suppose independent random samples are available from k ( k ≥ 2) exponential populations ∏ 1 ,...,∏ k with a common location θ and scale parameters σ 1 ,...,σ k , respectively. Let X i and Y i denote the minimum and the mean, respectively, of the i th sample, and further let X = min{ X 1 ,..., X k } and T i = Y i - X ; i = 1,..., k . For selecting a nonempty subset of {∏ 1 ,...,∏ k } containing the best population (the one associated with max{σ 1 ,...,σ k }), we use the decision rule which selects ∏ i if T i ≥ c max{ T 1 ,..., T k }, i = 1,..., k . Here 0 [ABSTRACT FROM AUTHOR]

Consider k (≥2) independent exponential populations Π1, Π2, … Πk, having the common unknown location parameter μ ∈ (-∞, ∞) (also called the guarantee time) and unknown scale parameters σ1, σ2, … σ k, respectively (also called the remaining mean lifetimes after the completion of guarantee times), σi > 0, I = 1, 2, … ,k. Assume that the correct ordering between σ1, σ2, …, k is not known apriori and let σ[i], I = 1, 2, …, k, denote the ith smallest of σ js, so that σ[1] ≤ σ[2] ··· ≤σ[k]. Then θ i = μ + σi is the mean lifetime of Πi, I = 1, 2, …, k. Let θ[1] ≤ θ[2] >>> ≤ θ[k] denote the ranked values of the θjs, so that θ[i] = μ + σ[i], I = 1, 2, …, k, and let Π(i) denote the unknown population associated with the ith smallest mean lifetime θ[i] = μ + σ[i], I = 1, 2, … k. Based on independent random samples from the k populations, we propose a selection procedure for the goal of selecting the population having the longest mean lifetime θ[k] (called the ‘best’ population), under the subset selection formulation. Tables for the implementation of the proposed selection procedure are provided. It is established that the proposed subset selection procedure is monotone for a general k (≥2). For k = 2, we consider the loss measured by the size of the selected subset and establish that the proposed subset selection procedure is minimax among selection procedures that satisfy a certain probability requirement (called the P*-condition) for the inclusion of the best population in the selected subset. [ABSTRACT FROM AUTHOR]

In this article, we propose a more general criterion called S[sub p]-criterion, for subset selection in the multiple linear regression Model. Many subset selection methods are based on the Least Squares (LS) estimator of β, but whenever the data contain an influential observation or the distribution of the error variable deviates from normality, the LS estimator performs 'poorly' and hence a method based on this estimator (for example, Mallows' C[sub p],-criterion) tends to select a 'wrong' subset. The proposed method overcomes this drawback and its main feature is that it can be used with any type of estimator (either the LS estimator or any robust estimator) of β without any need for modification of the proposed criterion. Moreover, this technique is operationally simple to implement as compared to other existing criteria. The method is illustrated with examples. [ABSTRACT FROM AUTHOR]