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通过非参数滤波估计随机波动率模型AI 解读

A two-step estimation method of stochastic volatility models is proposed: In the first step, we nonparametrically estimate the (unobserved) instantaneous volatility process. In the second step, standard estimation methods for fully observed diffusion processes are employed, but with the filtered/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle…

半参数 GARCH 模型的样条估计AI 解读

The semiparametric GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) model of Yang (2006, Journal of Econometrics 130, 365–384) has combined the flexibility of a nonparametric link function with the dependence on infinitely many past observations of the classic GARCH model. We propose a cubic spline procedure to estimate the unknown quantities in the semiparametric GARCH model that is intuitively appealing due to its simplicity. The theoretical properties of…

含单整时间序列的半参数光滑系数模型的一致非参数检验AI 解读

In this paper, we propose a simple nonparametric test for testing the null hypothesis of constant coefficients against nonparametric smooth coefficients in a semiparametric varying coefficient model with integrated time series. We establish the asymptotic distributions of the proposed test statistic under both null and alternative hypotheses. Moreover, we derive a central limit theorem for a degenerate second order U-statistic, which contains a mixture of stationary and…

双边市场的半参数分析：美国地方日报应用AI 解读

Summary This paper considers an empirical semiparametric model for two‐sided markets. Contrary to existing empirical literature on two‐sided markets, we specify network effects and probability distribution functions of net benefits of the two sides nonparametrically. We then estimate the model by nonparametric instrumental variables regression for local daily newspapers from the USA. We show that semiparametric specification is supported by the data and the network effects…

半竞争风险数据的层级模型：胰腺癌临终照护质量应用AI 解读

Readmission following discharge from an initial hospitalization is a key marker of quality of healthcare in the United States. For the most part, readmission has been studied among patients with “acute” health conditions, such as pneumonia and heart failure, with analyses based on a logistic-Normal generalized linear mixed model. Naïve application of this model to the study of readmission among patients with “advanced” health conditions such as pancreatic cancer, however, is…

基于模板的碱基对分辨率全基因组测序分析模型AI 解读

We consider the problem of estimating the genome-wide distribution of nucleosome positions from paired-end sequencing data. We develop a modeling approach based on nonparametric templates to control for the variability along the sequence of read counts associated with nucleosomal DNA due to enzymatic digestion and other sample preparation steps, and we develop a calibrated Bayesian method to detect local concentrations of nucleosome positions. We also introduce a set of…

小波系数之间的依赖包含多少信息？AI 解读

This article is motivated by several articles that propose statistical inference where the independence of wavelet coefficients for both short- as well as long-range dependent time series is assumed. We focus on the sample variance and investigate the influence of the dependence between wavelet coefficients and this statistic. To this end, we derive asymptotic distributional properties of the sample variance for a time series that is synthesized, ignoring some or all…

带序贯转移时间的动态治疗方案的贝叶斯非参数估计AI 解读

We analyze a dataset arising from a clinical trial involving multi-stage chemotherapy regimes for acute leukemia. The trial design was a 2 × 2 factorial for frontline therapies only. Motivated by the idea that subsequent salvage treatments affect survival time, we model therapy as a dynamic treatment regime (DTR), that is, an alternating sequence of adaptive treatments or other actions and transition times between disease states. These sequences may vary substantially…

贝叶斯非参数纵向数据分析AI 解读

Fernando A. Quintanaa*, Wesley O. Johnsonb, L. Elaine Waetjenc & Ellen B. Goldca Departamento de Estadística, Pontificia Universidad Católica de Chile Santiago, Chileb Department of Statistics, University of California Irvine, Irvine, CA, USAc Department of Obstetrics and Gynecology, and Department of Public Health Sciences, Division of Epidemiology, University of California Davis, Davis, CA, USACONTACT Fernando A. Quintana quintana@mat.uc.cl Departamento de Estadística,…

动态协方差模型AI 解读

An important problem in contemporary statistics is to understand the relationship among a large number of variables based on a dataset, usually with p, the number of the variables, much larger than n, the sample size. Recent efforts have focused on modeling static covariance matrices where pairwise covariances are considered invariant. In many real systems, however, these pairwise relations often change. To characterize the changing correlations in a high-dimensional system,…

由负二项过程族构造的随机计数矩阵先验AI 解读

We define a family of probability distributions for random count matrices with a potentially unbounded number of rows and columns. The three distributions we consider are derived from the gamma-Poisson, gamma-negative binomial, and beta-negative binomial processes, which we refer to generically as a family of negative-binomial processes. Because the models lead to closed-form update equations within the context of a Gibbs sampler, they are natural candidates for…

未知依赖结构下利用自助法检验对称性AI 解读

This article considers tests for symmetry of the one-dimensional marginal distribution of fractionally integrated processes. The tests are implemented by using an autoregressive sieve bootstrap approximation to the null sampling distribution of the relevant test statistics. The sieve bootstrap allows inference on symmetry to be carried out without knowledge of either the memory parameter of the data or of the appropriate norming factor for the test statistic and its…

非参数生产模型的假设检验AI 解读

Data envelopment analysis (DEA) and free disposal hull (FDH) estimators are widely used to estimate efficiency of production. Practitioners use DEA estimators far more frequently than FDH estimators, implicitly assuming that production sets are convex. Moreover, use of the constant returns to scale (CRS) version of the DEA estimator requires an assumption of CRS. Although bootstrap methods have been developed for making inference about the efficiencies of individual units,…

处理效应半参数估计中的模型平均AI 解读

Choosing the covariates and functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes a data-driven way of averaging the estimators over candidate specifications to resolve the specification uncertainty in the propensity score weighting estimation of the ATT. The proposed procedures minimize the estimated MSE of the ATT estimator in a local asymptotic framework. We formulate model averaging as a statistical decision…

半参数 Copula 依赖模型的拟合优度检验AI 解读

This paper concerns goodness-of-fit tests for semiparametric copula models. Our contribution is two-fold: we first propose a new test constructed via the comparison between “in-sample” and “out-of-sample” pseudo-likelihoods. Under the null hypothesis that the copula model is correctly specified, we show that the proposed test statistic converges in probability to a constant equal to the dimension of the parameter space. We establish the asymptotic normality and investigate…

持续且内生的微观结构效应与采样时长下证券协变矩阵的偏差校正AI 解读

I propose a bias-corrected non-parametric estimator of the covariation matrix of log security prices, designed as a convex combination of two realized kernels. The estimator is simple but possesses desirable statistical properties including consistency, asymptotic normality and the parametric rate of convergence in the presence of persistent, diurnally heteroskedastic and endogenous microstructure effects. It is robust to the asynchronous trading of multiple securities with…

含依赖及共同协变量的风险率函数核估计AI 解读

We propose a hazard model where dependence between events is achieved by assuming dependence between covariates. This model allows for correlated variables specific to observations as well as macro variables which all observations share. This setup better fits many economic and financial applications where events are not independent. Nonparametric estimation of the hazard function is then studied. Kernel estimators proposed in Nielsen and Linton (1995) and Linton et al.…

半参数两步模型的识别与估计AI 解读

Let H0(X) be a function that can be nonparametrically estimated. Suppose E [Y|X]=F0[X⊤β0, H0(X)]. Many models fit this framework, including latent index models with an endogenous regressor and nonlinear models with sample selection. We show that the vector β0 and unknown function F0 are generally point identified without exclusion restrictions or instruments, in contrast to the usual assumption that identification without instruments requires fully specified functional…

通过经验平衡校准加权实现平均处理效应的全局有效非参数推断AI 解读

Summary The estimation of average treatment effects based on observational data is extremely important in practice and has been studied by generations of statisticians under different frameworks. Existing globally efficient estimators require non-parametric estimation of a propensity score function, an outcome regression function or both, but their performance can be poor in practical sample sizes. Without explicitly estimating either function, we consider a wide class of…

稀疏序贯动态成像中的漂移估计及纳米荧光显微应用AI 解读

Summary A major challenge in many modern superresolution fluorescence microscopy techniques at the nanoscale lies in the correct alignment of long sequences of sparse but spatially and temporally highly resolved images. This is caused by the temporal drift of the protein structure, e.g. due to temporal thermal inhomogeneity of the object of interest or its supporting area during the observation process. We develop a simple semiparametric model for drift correction in…

双侧删失聚类数据共享脆弱性模型的半参数有效估计AI 解读

In this paper, we investigate frailty models for clustered survival data that are subject to both left- and right-censoring, termed "doubly-censored data". This model extends current survival literature by broadening the application of frailty models from right-censoring to a more complicated situation with additional left censoring. Our approach is motivated by a recent Hepatitis B study where the sample consists of families. We adopt a likelihood approach that aims at the…

非参数密度丝状结构估计的理论分析AI 解读

This paper provides a rigorous study of the nonparametric estimation of filaments or ridge lines of a probability density $f$. Points on the filament are considered as local extrema of the density when traversing the support of $f$ along the integral curve driven by the vector field of second eigenvectors of the Hessian of $f$. We “parametrize” points on the filaments by such integral curves, and thus both the estimation of integral curves and of filaments will be considered…

基于剖面优化的单指标分位数回归推断AI 解读

Single index models offer greater flexibility in data analysis than linear models but retain some of the desirable properties such as the interpretability of the coefficients. We consider a pseudo-profile likelihood approach to estimation and testing for single-index quantile regression models. We establish the asymptotic normality of the index coefficient estimator as well as the optimal convergence rate of the nonparametric function estimation. Moreover, we propose a score…

向量分位数回归：最优传输方法AI 解读

We propose a notion of conditional vector quantile function and a vector quantile regression. A conditional vector quantile function (CVQF) of a random vector $Y$, taking values in $\mathbb{R}^{d}$ given covariates $Z=z$, taking values in $\mathbb{R}^{k}$, is a map $u\longmapsto Q_{Y|Z}(u,z)$, which is monotone, in the sense of being a gradient of a convex function, and such that given that vector $U$ follows a reference non-atomic distribution $F_{U}$, for instance uniform…