Search

In recent years, Kriging model has gained wide popularity in various fields such as space geology, econometrics, and computer experiments. As a result, research on this model has proliferated. In this paper, the authors propose a model averaging estimation based on the best linear unbiased prediction of Kriging model and the leave-one-out cross-validation method, with consideration for the model uncertainty. The authors present a weight selection criterion for the model averaging estimation and provide two theoretical justifications for the proposed method. First, the estimated weight based on the proposed criterion is asymptotically optimal in achieving the lowest possible prediction risk. Second, the proposed method asymptotically assigns all weights to the correctly specified models when the candidate model set includes these models. The effectiveness of the proposed method is verified through numerical analyses. [ABSTRACT FROM AUTHOR]

Summary: Binary data subject to one type of misclassification exist in various fields. It is collected in a double‐sampling scheme that includes a gold standard test and a fallible test. The main parameter of interest for this type of data is the positive probability p$$ p $$ of the gold standard test. Existing intervals are unreliable because the given nominal level 1−α$$ 1-\alpha $$ is not achieved. In this paper, we construct an exact interval by inverting the E+M score tests and improve it by the general h$$ h $$‐function method. We find that the total length of the improved interval is shorter than the exact intervals that are also the improved intervals when we apply the h$$ h $$‐function to several existing approximate intervals, including the score and Bayesian intervals. Therefore, it is recommended for practice. We are also interested in two other parameters: p∗$$ {p}^{\ast } $$—the difference between the two positive rates for the fallible and gold standard tests—and ξ$$ \xi $$—the false positive rate for the fallible test. To the best of our knowledge, the research on these two parameters is limited. For p∗$$ {p}^{\ast } $$, we find that any interval for p$$ p $$ can be converted to an interval for p∗$$ {p}^{\ast } $$. So, the interval converted from the aforementioned recommended interval for p$$ p $$ is recommended for inferring p∗$$ {p}^{\ast } $$. For ξ$$ \xi $$, the improved interval by the h$$ h $$‐function method over the E+M score interval is derived. We use an example to illustrate how the intervals are computed and provide a real data analysis. [ABSTRACT FROM AUTHOR]

The 2×2$$ 2\times 2 $$ table with a structural zero represents a common scenario in clinical trials and epidemiology, characterized by a specific empty cell. In such cases, the risk ratio serves as a vital parameter for statistical inference. However, existing confidence intervals, such as those constructed through the score test and Bayesian methods, fail to achieve the prescribed nominal level. Our focus is on numerically constructing exact confidence intervals for the risk ratio. We achieve this by optimally combining the modified inferential model method and the h$$ h $$‐function method. The resulting interval is then compared with intervals generated by four existing methods: the score method, the exact score method, the Bayesian tailed‐based method and the inferential model method. This comparison is conducted based on the infimum coverage probability, average interval length and non‐coverage probability criteria. Remarkably, our proposed interval outperforms other exact intervals, being notably shorter. To illustrate the effectiveness of our approach, we discuss two examples in detail. [ABSTRACT FROM AUTHOR]

The weighted sum of binomial proportions and the interaction effect are two important cases of the linear combination of binomial proportions. Existing confidence intervals for these two parameters are approximate. We apply the h -function method to a given approximate interval and obtain an exact interval. The process is repeated multiple times until the final-improved interval (exact) cannot be shortened. In particular, for the weighted sum of two proportions, we derive two final-improved intervals based on the (approximate) adjusted score and fiducial intervals. After comparing several currently used intervals, we recommend these two final-improved intervals for practice. For the weighted sum of three proportions and the interaction effect, the final-improved interval based on the adjusted score interval should be used. Three real datasets are used to detail how the approximate intervals are improved. [ABSTRACT FROM AUTHOR]

The relative risk and odds ratio are widely used in many fields, including biomedical research, to compare two treatments. Extensive research has been done to infer the two parameters through approximate or exact confidence intervals. However, these intervals may be liberal or conservative. A natural question is whether the intervals can be further improved in maintaining the correct confidence coefficient of an approximate interval or shortening an exact but conservative interval. In this article, when two independent binomials are observed we offer an effort to improve any of the existing intervals by applying the h$$ h $$‐function method. In particular, if the given interval is approximate, then the improved interval is exact; if the given interval is exact, then the improved interval is a subset of the given interval. This method is also applied multiple times to the improved intervals until the final resultant interval cannot be shortened any further. To demonstrate the effectiveness of the method, we use three real datasets to illustrate in detail how several good intervals in practice are improved. Two exact intervals are then recommended for estimating each of the two parameters in different scenarios. [ABSTRACT FROM AUTHOR]

This paper studies the least squares model averaging methods for two non-nested linear models. It is proved that the Mallows model averaging weight of the true model is root-n consistent. Then the authors develop a penalized Mallows criterion which ensures that the weight of the true model equals 1 with probability tending to 1 and thus the averaging estimator is asymptotically normal. If neither candidate model is true, the penalized Mallows averaging estimator is asymptotically optimal. Simulation results show the selection consistency of the penalized Mallows method and the superiority of the model averaging approach compared with the model selection estimation. [ABSTRACT FROM AUTHOR]

Currently, working with partially observed functional data has attracted a greatly increasing attention, since there are many applications in which each functional curve may be observed only on a subset of a common domain, and the incompleteness makes most existing methods for functional data analysis ineffective. In this paper, motivated by the appealing characteristics of conditional quantile regression, the authors consider the functional linear quantile regression, assuming the explanatory functions are observed partially on dense but discrete point grids of some random subintervals of the domain. A functional principal component analysis (FPCA) based estimator is proposed for the slope function, and the convergence rate of the estimator is investigated. In addition, the finite sample performance of the proposed estimator is evaluated through simulation studies and a real data application. [ABSTRACT FROM AUTHOR]

Mechanical interactions between cells have been shown to play critical roles in regulating cell signaling and communications. However, the precise measurement of intercellular forces is still quite challenging, especially considering the complex environment at cell–cell junctions. In this study, we report a fluorescence lifetime‐based approach to image and quantify intercellular molecular tensions. Using this method, tensile forces among multiple ligand–receptor pairs can be measured simultaneously. We first validated our approach and developed lifetime measurement‐based DNA tension probes to image E‐cadherin‐mediated tension on epithelial cells. These probes were then further applied to quantify the correlations between E‐cadherin and N‐cadherin tensions during an epithelial–mesenchymal transition process. The modular design of these probes can potentially be used to study the mechanical features of various physiological and pathological processes. [ABSTRACT FROM AUTHOR]

Mechanical interactions between cells have been shown to play critical roles in regulating cell signaling and communications. However, the precise measurement of intercellular forces is still quite challenging, especially considering the complex environment at cell–cell junctions. In this study, we report a fluorescence lifetime‐based approach to image and quantify intercellular molecular tensions. Using this method, tensile forces among multiple ligand–receptor pairs can be measured simultaneously. We first validated our approach and developed lifetime measurement‐based DNA tension probes to image E‐cadherin‐mediated tension on epithelial cells. These probes were then further applied to quantify the correlations between E‐cadherin and N‐cadherin tensions during an epithelial–mesenchymal transition process. The modular design of these probes can potentially be used to study the mechanical features of various physiological and pathological processes. [ABSTRACT FROM AUTHOR]

In real data analysis, practitioners frequently come across the case that a discrete response will be related to both a function-valued random variable and a vector-value random variable as the predictor variables. In this paper, we consider the generalized functional partially linear models (GFPLM). The infinite slope function in the GFPLM is estimated by the principal component basis function approximations. Then, we consider the theoretical properties of the estimator obtained by maximizing the quasi likelihood function. The asymptotic normality of the estimator of the finite dimensional parameter and the rate of convergence of the estimator of the infinite dimensional slope function are established, respectively. We investigate the finite sample properties of the estimation procedure via Monte Carlo simulation studies and a real data analysis. [ABSTRACT FROM AUTHOR]

In this paper, we develop statistical inference procedures for functional quadratic quantile regression model in which the response is a scalar and the predictor is a random function defined on a compact set of R. The functional coefficients are estimated by functional principal components. The asymptotic properties of the resulting estimators are established under mild conditions. In order to test the significance of the nonlinear term in the model, we propose a rank score test procedure. The asymptotic properties of the proposed test statistic are established. The proposed method provides a highly efficient and robust alternative to the least squares method, and can be conveniently implemented using existing R software package. Finally, we examine the performance of the proposed method for finite sample sizes by Monte Carlo simulation studies and illustrate it with a real data example. [ABSTRACT FROM AUTHOR]

This paper investigates the hypothesis test of the parametric component in partial functional linear regression models. Based on a rank score function, the authors develop a rank test using functional principal component analysis, and establish the asymptotic properties of the resulting test under null and local alternative hypotheses. A simulation study shows that the proposed test procedure has good size and power with finite sample sizes. The authors also present an illustration through fitting the Berkeley Growth Data and testing the effect of gender on the height of kids. [ABSTRACT FROM AUTHOR]

Variable selection has played a fundamental role in regression analysis. Spatial autoregressive model is a useful tool in econometrics and statistics in which context variable selection is necessary but not adequately investigated. In this paper, we consider conducting variable selection in spatial autoregressive models with a diverging number of parameters. Smoothly clipped absolute deviation penalty is considered to obtain the estimators. Moreover the dimension of the covariates are allowed to vary with sample size. In order to attenuate the bias caused by endogeneity, instrumental variable is adopted in the estimation procedure. The proposed method can do parametric estimation and variable selection simultaneously. Under mild conditions, we establish the asymptotic and oracle property of the proposed estimators. Finally, the performance of the proposed estimation procedure is examined via Monte Carlo simulation studies and a data set from a Boston housing price is analyzed as an illustrative example. [ABSTRACT FROM AUTHOR]

M-estimation is a widely used technique for robust statistical inference. In this paper, we study model selection and model averaging for M-estimation to simultaneously improve the coverage probability of confidence intervals of the parameters of interest and reduce the impact of heavy-tailed errors or outliers in the response. Under general conditions, we develop robust versions of the focused information criterion and a frequentist model average estimator for M-estimation, and we examine their theoretical properties. In addition, we carry out extensive simulation studies as well as two real examples to assess the performance of our new procedure, and find that the proposed method produces satisfactory results. [ABSTRACT FROM AUTHOR]

Recently, researchers have been attempting to control pluripotent stem cell fate or generate self-organized tissues from stem cells. Advances in bioengineering enable generation of organotypic structures, which capture the cellular components, spatial cell organization and even some functions of tissues or organs in development. However, only a few engineering tools have been utilized to regulate the formation and organization of spatially complex tissues derived from stem cells. Here, we provide a review of recent progress in the culture of organotypic structures in vitro, focusing on how microengineering approaches including geometric confinement, extracellular matrix (ECM) property modulation, spatially controlled biochemical factors, and external forces, can be utilized to generate organotypic structures. Moreover, we will discuss potential technologies that can be applied to further control both soluble and insoluble factors spatiotemporally in vitro. In summary, advanced engineered approaches have a great promise in generating miniaturized tissues and organs in a reproducible fashion, facilitating the cellular and molecular understanding of embryogenesis and morphogenesis processes. [ABSTRACT FROM AUTHOR]

In this paper, we study model selection and model averaging for quantile regression with randomly right censored response. We consider a semi-parametric censored quantile regression model without distribution assumptions. Under general conditions, a focused information criterion and a frequentist model averaging estimator are proposed, and theoretical properties of the proposed methods are established. The performances of the procedures are illustrated by extensive simulations and the primary biliary cirrhosis data. [ABSTRACT FROM AUTHOR]

In this article, we study model selection and model averaging in quantile regression. Under general conditions, we develop a focused information criterion and a frequentist model average estimator for the parameters in quantile regression model, and examine their theoretical properties. The new procedures provide a robust alternative to the least squares method or likelihood method, and a major advantage of the proposed procedures is that when the variance of random error is infinite, the proposed procedure works beautifully while the least squares method breaks down. A simulation study and a real data example are presented to show that the proposed method performs well with a finite sample and is easy to use in practice. [ABSTRACT FROM AUTHOR]

We consider the statistical inference for right-censored data when censoring indicators are missing but nonignorable, and propose an adjusted imputation product-limit estimator. The proposed estimator is shown to be consistent and converges to a Gaussian process. Furthermore, we develop an empirical process-based testing method to check the MAR (missing at random) mechanism, and establish asymptotic properties for the proposed test statistic. To determine the critical value of the test, a consistent model-based bootstrap method is suggested. We conduct simulation studies to evaluate the numerical performance of the proposed method and compare it with existing methods. We also analyze a real data set from a breast cancer study for an illustration. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the model checking problem for a partial linear model when some covariates are missing at random. A weighted model-adjustment method is applied to estimate the regression coefficients and the nonparametric function for the null hypothetical partial linear model. A testing procedure based on a residual-marked empirical process is developed to check the adequacy of the partial linear model. It is shown that the proposed test is consistent and can detect the local alternatives converging to the null hypothetical model at the rate n −1/2. Since the asymptotic null distribution of the testing statistics is case-dependent, an adjusted wild bootstrap method is used to decide the critical value, which is proved to be consistent. A simulation study and a real data analysis are conducted to show that the proposed procedure works well. [ABSTRACT FROM AUTHOR]

a competitive depth, $$ \tilde L^2 $$-depth is modified from L-depth. Its induced median is called $$ \tilde L^2 $$-median. Basic properties of the median and its sample version are provided. Especially, the strong consistency of sample median is gained under weaker condition. Robustness of the median and its sample version is discussed. Besides ease of computation, it is shown that $$ \tilde L^2 $$-median has both good large-sample and robust properties. Simulation studies are also given to compare the breakdown point of $$ \tilde L^2 $$-median with that of other depth-induced medians. [ABSTRACT FROM AUTHOR]