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高维模型的双向同时推断AI 解读

This article proposes a general two-directional simultaneous inference (TOSI) framework for high-dimensional models with a manifest variable or latent variable structure, for example, high-dimensional mean models, high-dimensional sparse regression models, and high-dimensional latent factors models. TOSI performs simultaneous inference on a set of parameters from two directions, one to test whether the assumed zero parameters indeed are zeros and one to test whether exist…

超高维混合类型相关变量的广义因子模型AI 解读

As high-dimensional data measured with mixed-type variables gradually become prevalent, it is particularly appealing to represent those mixed-type high-dimensional data using a much smaller set of so-called factors. Due to the limitation of the existing methods for factor analysis that deal with only continuous variables, in this article, we develop a generalized factor model, a corresponding algorithm and theory for ultra-high dimensional mixed types of variables where both…

个体特异性协变量缺失模式下的回归分析AI 解读

It is increasingly common to collect data from heterogeneous sources in practice. Two major challenges complicate the statistical analysis of such data. First, only a small proportion of units have complete information across all sources. Second, the missing data patterns vary across individuals. Our motivating online-loan data have 93% missing covariates where the missing pattern is individual-specific. The existing regression analysis with missing covariates either are…

利用纵向盲法评估联合估计治疗效应与安慰剂效应AI 解读

In some therapeutic areas, treatment evaluation is frequently complicated by a possible placebo effect (i.e., the psychobiological effect of a patient's knowledge or belief of being treated). When a substantial placebo effect is likely to exist, it is important to distinguish the treatment and placebo effects in quantifying the clinical benefit of a new treatment. These causal effects can be formally defined in a joint causal model that includes treatment (e.g., new versus…

参数还是非参数？模型选择的参数性指数AI 解读

In model selection literature, two classes of criteria perform well asymptotically in different situations: Bayesian information criterion (BIC) (as a representative) is consistent in selection when the true model is finite dimensional (parametric scenario); Akaike’s information criterion (AIC) performs well in an asymptotic efficiency when the true model is infinite dimensional (nonparametric scenario). But there is little work that addresses if it is possible and how to…

多个线性回归模型的多重比较AI 解读

Research on multiple comparison during the past 50 years or so has focused mainly on the comparison of several population means. Several years ago, Spurrier considered the multiple comparison of several simple linear regression lines. He constructed simultaneous confidence bands for all of the contrasts of the simple linear regression lines over the entire range (-∞, ∞) when the models have the same design matrices. This article extends Spurrier's work in several directions.…

改进 Anscombe–Chow–Robbins 完全序贯抽样方案AI 解读

A new sequential sampling scheme is proposed in which, after an initial batch sample, sampling is continued in batches of data-dependent sizes (at most k such batches), and then one-at-a-time with a data-dependent stopping rule. This new scheme requires about the same sample size as the fully sequential Anscombe-Chow-Robbins (ACR) sampling scheme but substantially fewer sampling operations. The problem of constructing fixed-width confidence intervals for the mean of a normal…

多个位置参数极差的置信上界构造AI 解读

This article considers the construction of an upper confidence bound on the range of a set of k location parameters, such as the k treatment effects in a one-way layout. This confidence bound will be useful to the experimenter in assessing the “equivalence” of the location parameters. Some general theoretical results are presented and the case of normally distributed data is considered in detail, with accompanying tables of confidence bounds. Some examples of the application…