Asymptotic Distribution Research Articles

On the estimation of quantile treatment effects using a semiparametric propensity score

. This article considers the estimation of quantile treatment effects under the assumption of unconfoundedness given quasi-experimental data. We propose a semiparametric single-index method to estimate the propensity score. Our approach overcomes the curse of dimensionality issue of a nonparametric propensity score and can handle a moderately large dimension of covariates. It is more flexible than the parametric propensity score and thereby alleviates the possible model misspecification problem. We derive the asymptotic distribution of the quantile treatment effect estimator that is based on the semiparametric propensity score. We also propose a consistent variance estimator and construct the confidence intervals for the QTE estimator. Monte Carlo simulation results show that the proposed estimator performs well in finite samples and the confidence intervals have adequate coverage rates. We demonstrate the usefulness of our method by applying it to a study of the quantile treatment effects of college education on income.

Good sequences with uncountable spectrum and singular asymptotic distribution

Good sequences with uncountable spectrum and singular asymptotic distribution

Lancaster correlation: A new dependence measure linked to maximum correlation

AbstractWe suggest novel correlation coefficients which equal the maximum correlation for a class of bivariate Lancaster distributions while being only slightly smaller than maximum correlation for a variety of further bivariate distributions. In contrast to maximum correlation, however, our correlation coefficients allow for rank and moment‐based estimators which are simple to compute and have tractable asymptotic distributions. Confidence intervals resulting from these asymptotic approximations and the covariance bootstrap show good finite‐sample coverage. In a simulation, the power of asymptotic as well as permutation tests for independence based on our correlation measures compares favorably with competing methods based on distance correlation or rank coefficients for functional dependence, among others. Moreover, for the bivariate normal distribution, our correlation coefficients equal the absolute value of the Pearson correlation, an attractive feature for practitioners which is not shared by various competitors. We illustrate the practical usefulness of our methods in applications to two real data sets.

A Note on the Distribution of the Extreme Degrees of a Random Graph via the Stein-Chen Method

We offer an alternative proof, using the Stein-Chen method, of Bollobás’ theorem concerning the distribution of the extreme degrees of a random graph. Our proof also provides a rate of convergence of the extreme degree to its asymptotic distribution. The same method also applies in a more general setting where the probability of every pair of vertices being connected by edges depends on the number of vertices.

Scoring rules and performance, new analysis of expert judgment data

AbstractA review of scoring rules highlights the distinction between rewarding honesty and rewarding quality. This motivates the introduction of a scale‐invariant version of the Continuous Ranked Probability Score (CRPS) which enables statistical accuracy (SA) testing based on an exact rather than an asymptotic distribution of the density of convolutions. A recent data set of 6761 expert probabilistic forecasts for questions for which the actual values are known is used to compare performance. New insights include that (a) variance due to assessed variables dominates variance due to experts, (b) performance on mean absolute percentage error (MAPE) is weakly related to SA (c) scale‐invariant CRPS combinations compete with the Classical Model (CM) on SA and MAPE, and (d) CRPS is more forgiving with regard to SA than the CM as CRPS is insensitive to location bias.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National ToxicologyProgram.

Generalized Linear Model (GLM) Applications for the Exponential Dispersion Model Generated by the Landau Distribution

The exponential dispersion model (EDM) generated by the Landau distribution, denoted by EDM-EVF (exponential variance function), belongs to the Tweedie scale with power infinity. Its density function does not have an explicit form and, as of yet, has not been used for statistical aspects. Out of all EDMs belonging to the Tweedie scale, only two EDMs are steep and supported on the whole real line: the normal EDM with constant variance function and the EDM-EVF. All other absolutely continuous steep EDMs in the Tweedie scale are supported on the positive real line. This paper aims to accomplish an overall picture of all generalized linear model (GLM) applications belonging to the Tweedie scale by including the EDM-EVF. This paper introduces all GLM ingredients needed for its analysis, including the respective link function and total and scaled deviance. We study its analysis of deviance, derive the asymptotic properties of the maximum likelihood estimation (MLE) of the covariate parameters, and obtain the asymptotic distribution of deviance, using saddlepoint approximation. We provide numerical studies, which include estimation algorithm, simulation studies, and applications to three real datasets, and demonstrate that GLM using the EDM-EVF performs better than the linear model based on the normal EDM. An R package accompanies all of these.

Drift-flux correlation for bubbly to annular two-phase flows under microgravity conditions

In light of the substantial importance of accurate void fraction predictions for the engineering design and safety evaluation of two-phase systems utilized in space-related applications, this study is dedicated to the investigation of the drift-flux correlation specifically for microgravity conditions. The present study has collected 458 experimental void fraction data taken in microgravity bubbly to annular flows. The analysis of the collected experimental data evident that (1) the distribution parameters vary with the flow conditions, and (2) the drift velocities under microgravity conditions are exceedingly small. However, the distribution parameter models of the reviewed existing drift-flux correlations fail to accurately capture the variation of distribution parameters with flow conditions under microgravity conditions. Moreover, there is a lack of a simple yet effective way to model the drift velocity of microgravity two-phase flow. To overcome the above weaknesses, a new drift-flux correlation has been proposed by (1) taking the flow condition effect on the variation of asymptotic distribution parameters into consideration, and (2) employing the concept of effective body acceleration and considering the decay of drift velocity in annular flow. The newly proposed drift-flux correlation has been evaluated by checking against the collected data and shows good predictive ability.

Empirical likelihood inference for time-varying coefficient autoregressive models

In this paper, we propose time-varying coefficient autoregressive models (TVARMs) of [Formula: see text]th order as a solution for capturing the nonlinear structure of multiple explanatory variables. The stationary [Formula: see text]-mixing sequence of autoregressive variables is considered. We apply empirical likelihood approach to investigate confidence regions of the autoregressive coefficients of the TVARMs, and establish the nonparametric version of Wilks’ theorem of the proposed empirical log-likelihood ratio. Further, the maximum empirical likelihood estimator of the autoregressive coefficients and its asymptotic distribution are also obtained. Besides, to determine the order of autoregression, penalized empirical likelihood approach is developed with the help of smoothly clipped absolute deviation penalty and the resultant penalized estimators have the oracle property. Simulation studies and an analysis of monthly Chinese price index dataset are used to demonstrate the effectiveness of our proposed methods.

Confidence intervals in monotone regression

AbstractWe construct bootstrap confidence intervals for a monotone regression function. It has been shown that the ordinary nonparametric bootstrap, based on the nonparametric least squares estimator (LSE) , is inconsistent in this situation. We show that an ‐consistent bootstrap can be based on the smoothed , to be called the SLSE (Smoothed Least Squares Estimator). The asymptotic pointwise distribution of the SLSE is derived. The confidence intervals, based on the smoothed bootstrap, are compared to intervals based on the (not necessarily monotone) Nadaraya Watson estimator and the effect of Studentization is investigated. We also give a method for automatic bandwidth choice, correcting work in Sen and Xu (2015). Analogous methods for constructing confidence intervals in the current status model are discussed, improving on work in Groeneboom and Hendrickx (2018).

Parameter Estimation and Testing for the Doubly Geometric Process with Lognormal Distribution: Application to Bladder Cancer Patients’ Data

The geometric process (GP) has been widely utilized as a stochastic monotone model in the fields of probability and statistics. However, its practical application is often limited by certain assumptions. To address this, [Wu (2018). Doubly geometric process and applications. Journal of the Operational Research Society, 69(1), 66–67] introduced the doubly geometric process (DGP) as an extension of the GP model, relaxing some of its assumptions. Due to its ability to overcome the limitations of the GP model, the DGP has gained significant popularity in recent times. This study focuses on the parameter estimation problem for the DGP when the distribution of the first interarrival time follows a lognormal distribution with parameters [Formula: see text] and [Formula: see text]. We employ the maximum likelihood method to obtain estimates for both the model parameters and the distribution parameters. Additionally, we investigate the asymptotic joint distribution and statistical properties such as asymptotic unbiasedness and consistency of the estimators. Furthermore, we propose a novel test procedure to distinguish between the GP and DGP models. To assess the performance of the estimators and the proposed test procedure, we conduct a simulation study involving various sample sizes and parameter values. Finally, we present an application of the developed methods in fitting data from bladder cancer patients.

Pollution concentration monitoring using a new Birnbaum–Saunders control chart

AbstractAir pollution monitoring is an important issue in environmental science. The Birnbaum–Saunders (BS) distribution, originally applied to describe product failure time distribution to fatigue failures and general random wear failures, is also well to describe the pollutant concentration data due to accumulations of various pollutants in the air over time. Sulfur dioxide (SO2) is a critical factor in air pollution. Hence, it is important to monitor its concentration variation for air pollution prevention. Due to the complexity of its distribution form, there is no reliable and easy‐to‐use control chart for monitoring pollutant concentrations based on the BS distribution. We found that the SO2 concentration data follows the BS distribution. In this study, we propose a new median control chart based on the exact sampling distribution of the monitoring statistic to detect shifts in the median of BS distribution. Thus, given the false alarm rate, the control limits for such control charts can be obtained precisely satisfying a preset in‐control average run length using Monte Carlo simulations. The out‐of‐control average run lengths are calculated by simulation to evaluate the detection performance of the proposed chart when the median shifts occur. We further compare the detection performance of the proposed chart and those of the existing control charts based on asymptotic sampling distributions. In order to improve the detection ability of the proposed chart for small median shifts, an exponentially weighted moving average (EWMA) control chart is constructed. The results of numerical analyses demonstrated that the proposed EWMA chart performs much better than all existing control charts for monitoring the median of BS distribution. Finally, the proposed control charts are applied to monitor the median of SO2 concentrations for air pollution control, showing that both charts can effectively detect a shift in the median of SO2 concentrations. The proposed EWMA control chart even detects out a small shift in the median of SO2 concentrations. The results provide a continuous monitoring solution for air pollution prevention.

Tests under simple order in one‐way ANCOVA

AbstractAnalysis of covariance (ANCOVA) models are used when apart from the main treatments, some covariates also affect the response variable. In this article, the problem of testing the homogeneity of treatment effects against ordered alternatives is addressed in a fixed effects one‐way ANCOVA model when error variances are heterogeneous. The likelihood ratio test (LRT) and two approximate tests based on the asymptotic distributions of some union‐intersection type test statistics are proposed. A parametric bootstrap technique has been used to implement the LRT and its asymptotic validity is proved. A method to construct simultaneous confidence intervals is proposed. All the test procedures are further extended to the case of more than one covariate. The robustness of tests is also studied under departure from normality. Extensive simulation studies show that the proposed test procedures perform well in terms of achieving the nominal size value and good power values.

Spatial-sign-based high-dimensional white noises test

In this study, we explore the problem of hypothesis testing for white noise in high-dimensional settings, where the dimension of the random vector may exceed the sample sizes. We introduce a test procedure based on spatial-sign for high-dimensional white noise testing. This new spatial-sign-based test statistic is designed to emulate the test statistic proposed by Paindaveine and Verdebout [(2016). On high-dimensional sign tests. Bernoulli, 22(3), 1745–1769.], but under a more generalized scatter matrix assumption. We establish the asymptotic null distribution and provide the asymptotic relative efficiency of our test in comparison with the test proposed by Feng et al. [(2022). Testing for high-dimensional white noise. arXiv:2211.02964.] under certain specific alternative hypotheses. Simulation studies further validate the efficiency and robustness of our test, particularly for heavy-tailed distributions.

Change Point Test for Length-Biased Lognormal Distribution under Random Right Censoring

The length-biased lognormal distribution is a length-biased version of lognormal distribution, which is developed to model the length-biased lifetime data from, for example, biological investigation, medical research, and engineering fields. Owing to the existence of censoring phenomena in lifetime data, we study the change-point-testing problem of length-biased lognormal distribution under random censoring in this paper. A procedure based on the modified information criterion is developed to detect changes in parameters of this distribution. Under the sufficient condition of the Fisher information matrix being positive definite, it is proven that the null asymptotic distribution of the test statistic follows a chi-square distribution. In order to evaluate the uncertainty of change point location estimation, a way of calculating the coverage probabilities and average lengths of confidence sets of change point location based on the profile likelihood and deviation function is proposed. The simulations are conducted, under the scenarios of uniform censoring and exponential censoring, to investigate the validity of the proposed method. And the results indicate that the proposed approach performs better in terms of test power, coverage probabilities, and average lengths of confidence sets compared to the method based on the likelihood ratio test. Subsequently, the proposed approach is applied to the analysis of survival data from heart transplant patients, and the results show that there are differences in the median survival time post-heart transplantation among patients of different ages.

Robust change-point detection for functional time series based on U-statistics and dependent wild bootstrap

The aim of this paper is to develop a change-point test for functional time series that uses the full functional information and is less sensitive to outliers compared to the classical CUSUM test. For this aim, the Wilcoxon two-sample test is generalized to functional data. To obtain the asymptotic distribution of the test statistic, we prove a limit theorem for a process of U-statistics with values in a Hilbert space under weak dependence. Critical values can be obtained by a newly developed version of the dependent wild bootstrap for non-degenerate 2-sample U-statistics.

Multivariate differential association analysis

AbstractIdentifying how dependence relationships vary across different conditions plays a significant role in many scientific investigations. For example, it is important for the comparison of biological systems to see if relationships between genomic features differ between cases and controls. In this paper, we seek to evaluate whether relationships between two sets of variables are different or not across two conditions. Specifically, we assess: Do two sets of high‐dimensional variables have similar dependence relationships across two conditions? We propose a new kernel‐based test to capture the differential dependence. Specifically, the new test determines whether two measures that detect dependence relationships are similar or not under two conditions. We introduce the asymptotic permutation null distribution of the test statistic, and it is shown to work well under finite samples such that the test is computationally efficient, significantly enhancing its usability in analysing large datasets. We demonstrate through numerical studies that our proposed test has high power for detecting differential linear and non‐linear relationships. The proposed method is implemented in an package .

Simultaneous inference for proportions in multivariate stratified random sampling without replacement for service quality control using multiple choice questions

Determining the asymptotic distribution of the estimator for parameters of interest when employing complex sampling designs is a methodological issue of extreme importance. This paper addresses the problem of determining the asymptotic distribution of the vector of sample proportions when the survey questionnaire employs multiple-choice questions, as often done in service quality evaluations. Specifically, we consider a stratified sampling design where units within each stratum are selected without replacement, and the multivariate random variable from which the sample is drawn follows a multivariate Bernoulli distribution. The main goal is to obtain a valid asymptotic result under the specific conditions imposed by this design, so as to make statistical inference about the vector of specific parameters of interest, such as the proportion of responses assigned to each subdimension considered in the survey questionnaire.

Testing truncation dependence: The Gumbel–Barnett copula

In studies on lifetimes, occasionally, the population contains statistical units that are born before the data collection has started. Left-truncated are units that deceased before this start. For all other units, the age at the study start often is recorded and we aim at testing whether this second measurement is independent of the genuine measure of interest, the lifetime. Our basic model of dependence is the one-parameter Gumbel–Barnett copula. For simplicity, the marginal distribution of the lifetime is assumed to be Exponential and for the age-at-study-start, namely the distribution of birth dates, we assume a Uniform. Also for simplicity, and to fit our application, we assume that units that die later than our study period, are also truncated. As a result from point process theory, we can approximate the truncated sample by a Poisson process and thereby derive its likelihood. Identification, consistency and asymptotic distribution of the maximum-likelihood estimator are derived. Testing for positive truncation dependence must include the hypothetical independence which coincides with the boundary of the copula’s parameter space. By non-standard theory, the maximum likelihood estimator of the exponential and the copula parameter is distributed as a mixture of a two- and a one-dimensional normal distribution. For the proof, the third parameter, the unobservable sample size, is profiled out. An interesting result is, that it differs to view the data as truncated sample, or, as simple sample from the truncated population, but not by much. The application are 55 thousand double-truncated lifetimes of German businesses that closed down over the period 2014 to 2016. The likelihood has its maximum for the copula parameter at the parameter space boundary so that the p-value of test is 0.5. The life expectancy does not increase relative to the year of foundation. Using a Farlie–Gumbel–Morgenstern copula, which models positive and negative dependence, finds that life expectancy of German enterprises even decreases significantly over time. A simulation under the condition of the application suggests that the tests retain the nominal level and have good power.

Confidence Intervals for Piecewise Normal Distributions and Stochastic Variational Inequalities

In this paper, we first show how to obtain easy-to-compute confidence intervals for the center of a piecewise normal distribution given a sample from this distribution (assuming that the center belongs to a known affine set parallel to the common lineality space of all cones defining the piecewise normal distribution) by using certain skewed projectors on that space. We then extend this method to an asymptotic setting. Next, we apply this method to compute confidence intervals for the true solution of a stochastic variational inequality given a solution to a sample average approximation (SAA) problem for the general situation in which the asymptotic distribution of SAA solutions is piecewise normal. For stochastic complementarity problems, we obtain asymptotic normality of certain estimators of the true solution when the asymptotic distribution of the SAA solutions is piecewise normal. Funding: The research reported in this paper was supported the National Science Foundation [Grant DMS-1814894].