Optimum Designs for Parameter Estimation in Linear Mixture Models with Synergistic Effects

In this article, we derive optimum designs for parameter estimation in a mixture experiment when the response function is linear in the mixing components with some synergistic effects. The D- and A-optimality criteria have been used for the purpose. The Equivalence Theorem has been used to check for the optimality of the proposed designs.     

On Bayesian D-optimum Design Criteria and the Equivalence Theorem in Non-linear Models

The concavity of some Bayesian D -optimality criteria is investigated and is found in some cases to depend on the prior distribution. In the case of a non-concave criterion, the standard equivalence theorem may fail, but a local version continues to apply.     

Optimum Mixture Design Using Deficiency Criterion

In a mixture experiment the measured response is assumed to depend only on the relative proportion of ingredients or components present in the mixture. Scheffe (1958 Scheffe , H. ( 1958 ). Experiments with mixtures . J. R. Statist. Soc. B 20 : 344 – 360 . [Google Scholar], 1963 Scheffe , H. ( 1963 ). Simplex – centroid design for experiments with mixtures . J. R. Statist. Soc. B 25 : 235 – 263 . [Google Scholar]) first systematically considered this problem and introduced different models and designs suitable in such situations. Optimum designs for the estimation of parameters of different mixture models are available in the literature. The problem of estimating the optimum proportion of mixture components is of great practical importance. Pal and Mandal (2006 Pal , M. , Mandal , N. K. ( 2006 ). Optimum designs for optimum mixtures . Statist. Probab. Lett. 76 ( 13 ): 1369 – 1379 .[Crossref], [Web of Science ®] , [Google Scholar], 2007 Pal , M. , Mandal , N. K. (2007). Optimum mixture design via equivalence theory. communicated. JCISS 32:107–126. [Google Scholar]) attempted to find a solution to this problem by adopting a pseudo-Bayesian approach and using the trace criterion. Subsequently, Pal and Mandal (2008 Pal , M. , Mandal , N. K. ( 2008 ). Minimax designs for optimum mixtures . To appear in Statist. Probab. Lett.  [Google Scholar]) solved the problem using minimax criterion. In this article, the deficiency criterion due to Chatterjee and Mandal (1981 Chatterjee , S. K. , Mandal , N. K. ( 1981 ). Response surface designs for estimating the optimal point . Calcutta Statist. Assoc. Bull. . 30 : 145 – 169 .[Crossref] , [Google Scholar]) has been used as a measure for comparing the performance of competing designs.     

Optimum Designs for Estimation of Parameters in a Quadratic Mixture-Amount Model

The authors propose a mixture-amount model, which is quadratic both in the proportions of mixing components and the amount of mixture. They attempt to find the A- and D-optimal designs for the estimation of the model parameters within a subclass of designs. The optimality of the derived designs in the entire class of competing designs has been investigated through equivalence theorem.     

D-Optimum Designs for Optimum Mixture in a Quadratic Log Contrast Model

Optimal designs for estimating the optimum mixing proportions in a quadratic mixture model was first investigated by Pal and Mandal (2006). In this article, similar investigation is carried out when mean response in a mixture experiment is described by a quadratic log contrast model. It is found that in a symmetric subspace of the finite dimensional simplex, there exists a D-optimal design that puts weights at the centroid of the sub-space and the vertices of the experimental domain. The optimality is checked by numerical computation using Equivalence Theorem.     

Parameter Neutral Optimum Design for Non-linear Models

Some Bayesian approaches to D -optimum design of experiments are considered from the viewpoint of invariance under reparameterization of the underlying statistical model. An invariant criterion is proposed which does not require the detailed specification of a prior, and which is shown to be equivalent to G -optimality under a Jeffreys prior. The methods are applied and discussed in the contexts of exponential decay and quantal response models.     

In Search of the Optimum Harvey Wallbanger Recipe via Mixture Experiment Techniques

A Harvey Wallbanger is an alcoholic drink composed of three ingredients: orange juice, vodka, and Galliano. One of the authors (Sahrmann), a connoisseur of the libation, was perplexed upon discovering that different bartender handbooks listed different recipes for the drink. Determined to shed some light on this irksome problem, he issued invitations for an evening workshop session (party) to former and current attendees of an experimental-design training course taught within his company. A mixture experiment was designed and data collected using the class participants. This article describes the experimental design and the analysis of the data. The results of the experiment indicate that reducing the proportion of vodka produces better-flavored drinks. The results of the experiment were not entirely conclusive, however, so recommendations are made for future experimentation.     

Optimum Designs for Estimation of Optimum Amount and Proportions in a Mixture-Amount Model

This article studies a mixture-amount model, which is quadratic both in the proportions of mixing components and the amount of mixture. Using the pseudo-Bayesian approach of Pal and Mandal (2006 Pal , M. , Mandal , N. K. ( 2006 ). Optimum designs for optimum mixtures . Statist. Probab. Lett. 76 : 1369 – 1379 .[Crossref], [Web of Science ®] , [Google Scholar]), it attempts to find the A-optimal design for the estimation of the optimum mixing proportions and the optimum amount.     

Comments on the Mixture Detection Rule Used in SPC Control Charts

When calculating independently the false alarm rate of the eight usual runs rules used in SPC control chart, it appears that the proposed rule designed to detect mixture patterns corresponds to a Type-I error strongly lower than the seven other rules. This discrepancy is underlined and the mixture rule is showed to be useless both for in-control and out-of-control processes. Thus a modification of the mixture detection rule is proposed and the impact of this new mixture rule is then illustrated and discussed using Monte Carlo calculations.     

金融稳定与金融竞争力协同效应测度及其国际比较研究

在金融市场的发展过程中,政府以稳定为主与市场以竞争为主的导向之间存在对立统一关系,而这种对立统一的关系在管理中则需要实现两者的协同,方能产生协同效应。根据金融稳定与金融协同的内涵,分别构建评价金融稳定与金融竞争力的指标体系,利用典型相关对两者的协调性进行研究;同时以典型相关中的典型变量为基础,建立金融稳定与金融竞争力耦合度函数,利用耦合度函数对50个国家的协调度进行测度并进行国际比较,结果表明:金融稳定与金融竞争力具有高度相关性;金融稳定与金融竞争力协同效应分布和经济发展水平密切相关;金融稳定与金融竞争力的滞后表现形式多样。     

CDT-optimum designs for model discrimination, parameter estimation and estimation of a parametric function

After initiating the theory of optimal design by Smith (1918), many optimality criteria were introduced. Atkinson et al. (2007) used the definition of compound design criteria to combine two optimality criteria and introduced the DT- and CD-optimalities criteria. This paper introduces the CDT-optimum design that provides a specified balance between model discrimination, parameter estimation and estimation of a parametric function such as the area under curve in models for drug absorbance. An equivalence theorem is presented for the case of two models.     

Optimum points of stratification for two or more stage designs

In this paper, an attempt is made to obtain optimum points of stratification for two or more stage designs with equal p.s.u.'s and the subsequent units. Stratification on the auxiliary variable when the study variable is closely related to the auxiliary variable has also been obtained. The determination of OPS in these cases have been illustrated with the help of some known specific distributions.     

Bayesian Confidence Interval for the Difference of Two Proportions in the Matched-Paired Design

This article studies the construction of Bayesian confidence interval for the difference of two proportions in the matched-pair design, and applies it to the equiva-lence or non inferiority test. Under the Dirichlet prior distribution, the exact posterior distribution of difference of two proportions is derived. The tail confidence interval and the highest posterior density (HPD) interval are studied, and their frequentist performance are investigated by simulation in terms of the mean coverage probability of interval. Our results suggest to use tail interval at Jeffreys prior for testing equivalence or non inferiority in matched-pair design.     

Improving The Efficiency of The Case-Crossover Design

The case-crossover design has been used by many researchers to study the transient effect of an exposure on the risk of a rare outcome. In a case-crossover design, only cases are sampled and each case will act as his/her own control. The time of failure acts as the case and non failure times act as the controls. Case-crossover designs have frequently been used to study the effect of environmental exposures on rare diseases or mortality. Time trends and seasonal confounding may be present in environmental studies and thus need to be controlled for by the sampling design. Several sampling methods are available for this purpose. In time-stratified sampling, disjoint strata of equal size are formed and the control times within the case stratum are used for comparison. The random semi-symmetric sampling design randomly selects a control time for comparison from two possible control times. The fixed semi-symmetric sampling design is a modified version of the random semi-symmetric sampling design that removes the random selection. Simulations show that the fixed semi-symmetric sampling design improves the variance of the random semi-symmetric sampling estimator by at least 35% for the exposures we studied. We derive expressions for the asymptotic variance of risk estimators for these designs, and show, that while the designs are not theoretically equivalent, in many realistic situations, the random semi-symmetric sampling design has similar efficiency to a time-stratified sampling design of size two and the fixed semi-symmetric sampling design has similar efficiency to a time-stratified sampling design of size three.     

Estimating the Intercept in an Orthogonally Blocked Experiment when the Block Effects are Random

Abstract

For an orthogonally blocked experiment, Khuri [Khuri, A. I. (1992). Response surface models with random block effects. Technometrics 34:26–37] has shown that the ordinary least squares estimator, the generalized least squares estimator and the intra-block estimator of the factor effects in a response surface model with random block effects coincide. The ordinary least squares estimator ignores the blocks, whereas the generalized least squares and the intra-block estimators treat the block effects as random and fixed, respectively. As shown in this paper, the equivalence does not hold for the estimation of the intercept when the block sizes are heterogeneous. Practical examples are given to illustrate the theoretical results.     

Trajectory Modeling of Longitudinal Binary Data: Application of the EM Algorithm for Mixture Models

A developmental trajectory describes the course of behavior over time. Identifying multiple trajectories within an overall developmental process permits a focus on subgroups of particular interest. We introduce a framework for identifying trajectories by using the Expectation-Maximization (EM) algorithm to fit semiparametric mixtures of logistic distributions to longitudinal binary data. For performance comparison, we consider full maximization algorithms (PROC TRAJ in SAS), standard EM, and two other EM-based algorithms for speeding up convergence. Simulation shows that EM methods produce more accurate parameter estimates. The EM methodology is illustrated with a longitudinal dataset involving adolescents smoking behaviors.     

A Finite Mixture Three-Parameter Weibull Model for the Analysis of Wind Speed Data

This article presents a mixture three-parameter Weibull distribution to model wind speed data. The parameters are estimated by using maximum likelihood (ML) method in which the maximization problem is regarded as a nonlinear programming with only inequality constraints and is solved numerically by the interior-point method. By applying this model to four lattice-point wind speed sequences extracted from National Centers for Environmental Prediction (NCEP) reanalysis data, it is observed that the mixture three-parameter Weibull distribution model proposed in this paper provides a better fit than the existing Weibull models for the analysis of wind speed data under study.     

The Analysis of Methadone Clinic Data Using Marginal and Conditional Logistic Models with Mixture or Random Effects

The paper develops methods for the statistical analysis of outcomes of methadone maintenance treatment (MMT). Subjects for this study were a cohort of patients entering MMT in Sydney in 1986. Urine drug tests on these subjects were performed weekly during MMT, and were reported as either positive or negative for morphine, the marker of recent heroin use. To allow correlation between the repeated binary measurements, a marginal logistic model was fitted using the generalized estimating equation (GEE) approach and the alternating logistic regression approach. Conditional logistic models are also considered. Results of separate fitting to each patient and score tests suggest that there is substantial between-patient variation in response to MMT. To account for the population heterogeneity and to facilitate subject-specific inference, the conditional logistic model is extended by introducing random intercepts. The two, three and four group mixture models are also investigated. The model of best fit is a three group mixture model, in which about a quarter of the subjects have a poor response to MMT, with continued heroin use independent of daily dose of methadone; about a quarter of the subjects have a very good response, with little or no heroin use, again independent of dose; and about half the subjects responded in a dose-dependent fashion, with reduced heroin use while receiving higher doses of methadone. These findings are consistent with clinical experience. There is also an association between reduced drug use and increased duration in treatment. The mixture model is recommended since it is quite tractable in terms of estimation and model selection as well as being supported by clinical experience.     

Modeling the Factors Influencing the Evaluations of Items Through Mixture Models for Preference Datasets

In numerous situations, we use ranks dataset to exhibit preferences of a group of respondents towards a set of items. While assigning ranks, judges may consider several factors contributing to overall ranks of items. In this study, an attempt is made to model factors influencing the judges’ evaluations of items through mixture models for preference datasets. Both the probabilistic features of the mixture distribution and inferential as well as computational issues emerging out of the maximum likelihood estimation are addressed. Moreover, empirical evidences from observed dataset confirming the plausibility of the proposed model to preference dataset are provided.     

Constructions of Residual Treatment Effects Designs for Comparing Test Treatments with a Control

This article deals with residual treatment effects designs for the purpose of comparing v test treatments with a control treatment when the number of periods is no larger than v + 1. Control balanced residual treatment effects designs, which are Schur-optimal, are considered. Some methods of their construction are given.