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使用协变量的回归断点设计AI 解读

We study regression discontinuity designs when covariates are included in the estimation. We examine local polynomial estimators that include discrete or continuous covariates in an additive separable way, but without imposing any parametric restrictions on the underlying population regression functions. We recommend a covariate-adjustment approach that retains consistency under intuitive conditions and characterize the potential for estimation and inference improvements. We…

充分方向因子模型及其在基因表达数量性状位点发现中的应用AI 解读

Rapid improvement in technology has made it relatively cheap to collect genetic data, however statistical analysis of existing data is still much cheaper. Thus, secondary analysis of single-nucleotide polymorphism, SNP, data, i.e., reanalysing existing data in an effort to extract more information, is an attractive and cost-effective alternative to collecting new data. We study the relationship between gene expression and SNPs through a combination of factor analysis and…

回归残差过程的自助法：是否需要平滑？AI 解读

In this paper we consider a location model of the form $Y = m(X) + \varepsilon$, where $m(\cdot)$ is the unknown regression function, the error $\varepsilon$ is independent of the $p$-dimensional covariate $X$ and $E(\varepsilon)=0$. Given i.i.d. data $(X_1,Y_1),\ldots,(X_n,Y_n)$ and given an estimator $\hat m(\cdot)$ of the function $m(\cdot)$ (which can be parametric or nonparametric of nature), we estimate the distribution of the error term $\varepsilon$ by the empirical…

一般贝叶斯更新与损失似然自助法AI 解读

In this paper we revisit the weighted likelihood bootstrap, a method that generates samples from an approximate Bayesian posterior of a parametric model. We show that the same method can be derived, without approximation, under a Bayesian nonparametric model with the parameter of interest defined through minimizing an expected negative loglikelihood under an unknown sampling distribution. This interpretation enables us to extend the weighted likelihood bootstrap to posterior…

基于稀疏充分降维的稳健高效因果推断AI 解读

A fundamental assumption used in causal inference with observational data is that treatment assignment is ignorable given measured confounding variables. This assumption of no missing confounders is plausible if a large number of baseline covariates are included in the analysis, as we often have no prior knowledge of which variables can be important confounders. Thus, estimation of treatment effects with a large number of covariates has received considerable attention in…

倾向得分的设定检验AI 解读

This paper proposes new nonparametric diagnostic tools to assess the asymptotic validity of different treatment effects estimators that rely on the correct specification of the propensity score. We derive a particular restriction relating the propensity score distribution of treated and control groups, and develop specification tests based upon it. The resulting tests do not suffer from the “curse of dimensionality” when the vector of covariates is high-dimensional, are…

存在未观测拍卖异质性时第一价格拍卖风险厌恶的识别与估计AI 解读

This paper shows point-identification in first-price auctions with risk aversion and unobserved auction heterogeneity, by exploiting multiple bids per auction and variation in the number of bidders. If the exclusion restriction required for point-identification is violated, the recovered primitives are still valid bounds under weaker restrictions. We propose a Sieve Maximum Likelihood Estimator (SMLE). Monte Carlo experiments illustrate that the estimator performs well and…

离散选择模型条件矩不等式的降维推断AI 解读

This paper studies inference of preference parameters in semiparametric discrete choice models when these parameters are not point-identified and the identified set is characterized by a class of conditional moment inequalities. Exploring the semiparametric modeling restrictions, we show that the identified set can be equivalently formulated by moment inequalities conditional on only two continuous indexing variables. Such formulation holds regardless of the covariate…

利用离散样本估计连续时间随机过程的长期方差AI 解读

This paper develops the methodology and asymptotic theory for the estimation of longrun variance of continuous time process. We analyze the asymptotic bias and variance of the longrun variance estimator in continuous time, and provide the optimal bandwidth balancing them off and minimizing the asymptotic mean squared error. In the paper, we present not only how to consistently estimate the longrun variance of continuous time process, but also how to choose bandwidth…

面板反事实的参数、半参数与非参数构造AI 解读

Summary We consider panel parametric, semiparametric and nonparametric methods of constructing counterfactuals. We show through extensive simulations that no method is able to dominate other methods in all circumstances, since the true data‐generating process is typically unknown. We therefore also suggest a model‐averaging method as a robust method to generate counterfactuals. As an illustration of the sensitivity of counterfactual construction, we reexamine the impact of…

利用测验分数控制能力AI 解读

Summary This paper proposes a semiparametric method to control for ability using standardized test scores, or other item response assessments, in a regression model. The proposed method is based on a model in which the parameter of interest is invariant to monotonic transformations of ability. I show that the estimator is consistent as both the number of observations and the number of items on the test grow to infinity. I also derive conditions under which this estimator is…

向量时间序列共线性检验AI 解读

Summary We investigate the collinearity of vector time series in the frequency domain, by examining the rank of the spectral density matrix at a given frequency of interest. Rank reduction corresponds to collinearity at the given frequency. When the time series is nonstationary and has been differenced to stationarity, collinearity corresponds to co-integration at a particular frequency. We examine rank through the Schur complements of the spectral density matrix, testing…

具有非参数先验的多变点面板模型：贝叶斯推断与预测AI 解读

Change point models using hierarchical priors have been very successful estimating the parameter values of short-lived regimes. However, hierarchical priors have been parametric which leads to shrinkage in the estimates of extraordinary regime parameters. We overcome this by modeling the hierarchical priors nonparametrically. We also extend the change point to a panel of change point processes where the prior shares in the probabilities of changing regimes. When applied to…

优化的断点回归设计AI 解读

The increasing popularity of regression discontinuity methods for causal inference in observational studies has led to a proliferation of different estimating strategies, most of which involve first fitting nonparametric regression models on both sides of a treatment assignment boundary and then reporting plug-in estimates for the effect of interest. In applications, however, it is often difficult to tune the nonparametric regressions in a way that is well calibrated for the…

动态正电子发射断层扫描数据的函数型分析AI 解读

One application of positron emission tomography (PET), a nuclear imaging technique, in neuroscience involves in vivo estimation of the density of various proteins (often, neuroreceptors) in the brain. PET scanning begins with the injection of a radiolabeled tracer that binds preferentially to the target protein; tracer molecules are then continuously delivered to the brain via the bloodstream. By detecting the radioactive decay of the tracer over time, dynamic PET data are…

序列相关函数数据的贝叶斯半参数函数混合模型：青光眼数据应用AI 解读

Glaucoma, a leading cause of blindness, is characterized by optic nerve damage related to intraocular pressure (IOP), but its full etiology is unknown. Researchers at UAB have devised a custom device to measure scleral strain continuously around the eye under fixed levels of IOP, which here is used to assess how strain varies around the posterior pole, with IOP, and across glaucoma risk factors such as age. The hypothesis is that scleral strain decreases with age, which…

癌种特异性发病年龄外显率的贝叶斯半参数估计：Li-Fraumeni综合征应用AI 解读

Penetrance, which plays a key role in genetic research, is defined as the proportion of individuals with the genetic variants (i.e., genotype) that cause a particular trait and who have clinical symptoms of the trait (i.e., phenotype). We propose a Bayesian semiparametric approach to estimate the cancer-specific age-at-onset penetrance in the presence of the competing risk of multiple cancers. We employ a Bayesian semiparametric competing risk model to model the duration…

分布变化的序贯非参数检验：放射性异常检测应用AI 解读

We propose a sequential nonparametric test for detecting a change in distribution, based on windowed Kolmogorov–Smirnov statistics. The approach is simple, robust, highly computationally efficient, easy to calibrate, and requires no parametric assumptions about the underlying null and alternative distributions. We show that both the false-alarm rate and the power of our procedure are amenable to rigorous analysis, and that the method outperforms existing sequential testing…

基于增量倾向得分干预的非参数因果效应AI 解读

Most work in causal inference considers deterministic interventions that set each unit’s treatment to some fixed value. However, under positivity violations these interventions can lead to nonidentification, inefficiency, and effects with little practical relevance. Further, corresponding effects in longitudinal studies are highly sensitive to the curse of dimensionality, resulting in widespread use of unrealistic parametric models. We propose a novel solution to these…

变系数部分线性模型的Mallows型模型平均估计AI 解读

In the last decade, significant theoretical advances have been made in the area of frequentist model averaging (FMA); however, the majority of this work has emphasized parametric model setups. This article considers FMA for the semiparametric varying-coefficient partially linear model (VCPLM), which has gained prominence to become an extensively used modeling tool in recent years. Within this context, we develop a Mallows-type criterion for assigning model weights and prove…

一种通用的确定独立筛选程序AI 解读

Wenliang Pana, Xueqin Wanga, Weinan Xiaoa & Hongtu Zhub*a Department of Statistical Science, School of Mathematics, Sun Yat-Sen University, Guangzhou, P. R. Chinab Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TXCONTACT Hongtu Zhu hzhu5@mdanderson.org Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030Supplementary materials for this article are available online. Please go to…

高维时间序列的趋势检验AI 解读

The article considers statistical inference for trends of high-dimensional time series. Based on a modified L2 distance between parametric and nonparametric trend estimators, we propose a de-diagonalized quadratic form test statistic for testing patterns on trends, such as linear, quadratic, or parallel forms. We develop an asymptotic theory for the test statistic. A Gaussian multiplier testing procedure is proposed and it has an improved finite sample performance. Our…

社区数量未知时的概率社区检测AI 解读

A fundamental problem in network analysis is clustering the nodes into groups which share a similar connectivity pattern. Existing algorithms for community detection assume the knowledge of the number of clusters or estimate it a priori using various selection criteria and subsequently estimate the community structure. Ignoring the uncertainty in the first stage may lead to erroneous clustering, particularly when the community structure is vague. We instead propose a…

平滑变化均值趋势下的方差变点检测及肝脏获取应用AI 解读

Literature on change point analysis mostly requires a sudden change in the data distribution, either in a few parameters or the distribution as a whole. We are interested in the scenario, where the variance of data may make a significant jump while the mean changes in a smooth fashion. The motivation is a liver procurement experiment monitoring organ surface temperature. Blindly applying the existing methods to the example can yield erroneous change point estimates since the…