Mixture and mixture–process variable experiments for pharmaceutical applications

Many experiments in research and development in the pharmaceutical industry involve mixture components. These are experiments in which the experimental factors are the ingredients of a mixture and the response variable is a function of the relative proportion of each ingredient, not its absolute amount. Thus the mixture ingredients cannot be varied independently. A common variation of the mixture experiment occurs when there are also one or more process factors that can be varied independently of each other and of the mixture components, leading to a mixture–process variable experiment. We discuss the design and analysis of these types of experiments, using tablet formulation as an example. Our objective is to encourage greater utilization of these techniques in pharmaceutical research and development. Copyright © 2004 John Wiley & Sons Ltd.     

自适应多波束形成的算法

论述了一种窄带和宽带多波束形成的算法,这种算法所依据的准则是在波束满足指向和振幅要求的约束条件下使阵列输出功率最小。对于 N 元阵列,可同时形成 N-1个独立波束。根据对约束条件的不同控制,利用这种算法可实现波束扫描,形成宽波束、实现干扰对消或旁瓣对消等。同时还给出了所述算法的计算机模拟结果,结果表明所述算法是很有效的。     

The Design And Analysis of a Mixture Experiment on Glass Durability

This paper discusses the statistical design and analysis of a non–standard constrained mixture experiment on glass durability (the components sum to 1, and there are non–trivial lower or upper bounds on components or combinations of them). It notes some of the difficulties which arose during the study, and shows how the aims and methods of the experiment changed during its execution.     

带包络约束的优化滤波器设计方法

对信道均衡、雷达或声纳脉冲压缩等应用领域中要求满足波形条件且使输出噪声增益最小化的滤波器设计问题进行了研究。用波形包络约束表达式和极值条件可以准确地描述滤波器的性能要求,从而将该设计问题转化为半无限二次凸规划问题进行分析。利用Lagrangian对偶理论和Carathéodory维度理论把半无限二次凸规划问题转化成等价的易于求解的有限维对偶优化问题,并给出了求解有限维对偶优化问题的迭代算法,设计实例表明了此方法的有效性。     

论语言适应论的语境生成观

语言适应论框架下的语境论是一种以适应性为核心的生成观,它认为语境不是给定的,而是一个动态的受限生成过程。这种生成观将语境的建构性、自组织性、受限性与涌现性的关系的认识,提出到一个新的理论层面,它具有三个显著特征:1."生成"是受限的;2.语境生成的"过程"重于生成的"结果";3."生成"是涌现的。     

Stochastic Model Specification Search for Time-Varying Parameter VARs

This article develops a new econometric methodology for performing stochastic model specification search (SMSS) in the vast model space of time-varying parameter vector autoregressions (VARs) with stochastic volatility and correlated state transitions. This is motivated by the concern of overfitting and the typically imprecise inference in these highly parameterized models. For each VAR coefficient, this new method automatically decides whether it is constant or time-varying. Moreover, it can be used to shrink an otherwise unrestricted time-varying parameter VAR to a stationary VAR, thus providing an easy way to (probabilistically) impose stationarity in time-varying parameter models. We demonstrate the effectiveness of the approach with a topical application, where we investigate the dynamic effects of structural shocks in government spending on U.S. taxes and gross domestic product (GDP) during a period of very low interest rates.     

Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context

When employing model selection methods with oracle properties such as the smoothly clipped absolute deviation (SCAD) and the Adaptive Lasso, it is typical to estimate the smoothing parameter by m-fold cross-validation, for example, m = 10. In problems where the true regression function is sparse and the signals large, such cross-validation typically works well. However, in regression modeling of genomic studies involving Single Nucleotide Polymorphisms (SNP), the true regression functions, while thought to be sparse, do not have large signals. We demonstrate empirically that in such problems, the number of selected variables using SCAD and the Adaptive Lasso, with 10-fold cross-validation, is a random variable that has considerable and surprising variation. Similar remarks apply to non-oracle methods such as the Lasso. Our study strongly questions the suitability of performing only a single run of m-fold cross-validation with any oracle method, and not just the SCAD and Adaptive Lasso.     

Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain

We develop results for the use of Lasso and post‐Lasso methods to form first‐stage predictions and estimate optimal instruments in linear instrumental variables (IV) models with many instruments, p. Our results apply even when p is much larger than the sample size, n. We show that the IV estimator based on using Lasso or post‐Lasso in the first stage is root‐n consistent and asymptotically normal when the first stage is approximately sparse, that is, when the conditional expectation of the endogenous variables given the instruments can be well‐approximated by a relatively small set of variables whose identities may be unknown. We also show that the estimator is semiparametrically efficient when the structural error is homoscedastic. Notably, our results allow for imperfect model selection, and do not rely upon the unrealistic “beta‐min” conditions that are widely used to establish validity of inference following model selection (see also Belloni, Chernozhukov, and Hansen (2011b)). In simulation experiments, the Lasso‐based IV estimator with a data‐driven penalty performs well compared to recently advocated many‐instrument robust procedures. In an empirical example dealing with the effect of judicial eminent domain decisions on economic outcomes, the Lasso‐based IV estimator outperforms an intuitive benchmark. Optimal instruments are conditional expectations. In developing the IV results, we establish a series of new results for Lasso and post‐Lasso estimators of nonparametric conditional expectation functions which are of independent theoretical and practical interest. We construct a modification of Lasso designed to deal with non‐Gaussian, heteroscedastic disturbances that uses a data‐weighted ℓ₁‐penalty function. By innovatively using moderate deviation theory for self‐normalized sums, we provide convergence rates for the resulting Lasso and post‐Lasso estimators that are as sharp as the corresponding rates in the homoscedastic Gaussian case under the condition that logp = o(n^1/3). We also provide a data‐driven method for choosing the penalty level that must be specified in obtaining Lasso and post‐Lasso estimates and establish its asymptotic validity under non‐Gaussian, heteroscedastic disturbances.     

Editorial

This paper uses the empirical characteristic function (ECF) procedure to estimate the parameters of mixtures of normal distributions. Since the characteristic function is uniformly bounded, the procedure gives estimates that are numerically stable. It is shown that, using Monte Carlo simulation, the finite sample properties of th ECF estimator are very good, even in the case where the popular maximum likelihood estimator fails to exist. An empirical application is illustrated using the monthl excess return of the Nyse value-weighted index.