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Current treatment for patients with metastatic hormone-sensitive prostate cancer (mHSPC) delays disease progression and improves survival, but resistance is inevitable. Additional therapies that prolong survival are needed. Androgen deprivation therapy (ADT) combined with next-generation hormonal agents, such as enzalutamide, is standard-of-care for men with mHSPC. Emerging evidence suggests potential synergism between enzalutamide and the PD-1 inhibitor pembrolizumab in prostate cancer. The phase III randomized, placebo-controlled, double-blind KEYNOTE-991 trial will investigate the efficacy and safety of pembrolizumab versus placebo in combination with enzalutamide when initiating ADT in participants with mHSPC naive to next-generation hormonal agents. Approximately 1232 patients will be randomly assigned 1:1 to receive pembrolizumab 200 mg every 3 weeks or placebo every 3 weeks, both with enzalutamide 160 mg once daily and ADT. Dual primary end points are overall survival and radiographic progression-free survival. Secondary end points include time to first subsequent therapy, time to symptomatic skeletal related event, objective response rate and safety and tolerability. Clinical Trial Registration:NCT04191096 (ClinicalTrials.gov). The randomized, double-blind, phase III KEYNOTE-991 study will investigate the efficacy and safety of pembrolizumab versus placebo in combination with enzalutamide when initiating androgen deprivation therapy in participants with metastatic hormone sensitive prostate cancer naive to next-generation hormonal agents. [ABSTRACT FROM AUTHOR]

We consider the estimation of the error distribution function of partial linear single-index models. The estimation methods for the error distribution function based on the classical empirical distribution function as well as empirical likelihood method are discussed, the latter method allows for incorporation of additional information on the error distribution function into estimation. We show weak convergence of the corresponding empirical processes to Gaussian processes and compare both approaches with the asymptotic theory and by means of simulation studies. [ABSTRACT FROM AUTHOR]

Farinelli and Tibiletti (F–T) ratio, a general risk-reward performance measurement ratio, is popular due to its simplicity and yet generality that both Omega ratio and upside potential ratio are its special cases. The F–T ratios are ratios of average gains to average losses with respect to a target, each raised by a power index, p and q. In this paper, we establish the consistency of F–T ratios with any nonnegative values p and q with respect to first-order stochastic dominance. Second-order stochastic dominance does not lead to F–T ratios with any nonnegative values p and q, but can lead to F–T dominance with any p < 1 and q ≥ 1 . Furthermore, higher-order stochastic dominance ( n ≥ 3 ) leads to F–T dominance with any p < 1 and q ≥ n - 1 . We also find that when the variables being compared belong to the same location-scale family or the same linear combination of location-scale families, we can get the necessary relationship between the stochastic dominance with the F–T ratio after imposing some conditions on the means. There are many advantages of using the F–T ratio over other measures, and academics and practitioners can benefit by using the theory we developed in this paper. For example, the F–T ratio can be used to detect whether there is any arbitrage opportunity in the market, whether there is any anomaly in the market, whether the market is efficient, whether there is any preference of any higher-order moment in the market, and whether there is any higher-order stochastic dominance in the market. Thus, our findings enable academics and practitioners to draw better decision in their analysis. [ABSTRACT FROM AUTHOR]

We study the quantile estimation methods for the distortion measurement error data when variables are unobserved and distorted with additive errors by some unknown functions of an observable confounding variable. After calibrating the error-prone variables, we propose the quantile regression estimation procedure and composite quantile estimation procedure. Asymptotic properties of the proposed estimators are established, and we also investigate the asymptotic relative efficiency compared with the least-squares estimator. Simulation studies are conducted to evaluate the performance of the proposed methods, and a real dataset is analyzed as an illustration. [ABSTRACT FROM AUTHOR]

This paper is devoted to test the parametric single-index structure of the underlying model when there are outliers in observations. First, a test that is robust against outliers is suggested. The Hampel’s second-order influence function of the test statistic is proved to be bounded. Second, the test fully uses the dimension reduction structure of the hypothetical model and automatically adapts to alternative models when the null hypothesis is false. Thus, the test can greatly overcome the dimensionality problem and is still omnibus against general alternative models. The performance of the test is demonstrated by both Monte Carlo simulation studies and an application to a real dataset. [ABSTRACT FROM AUTHOR]

Abstract: Non‐parametric generalized likelihood ratio test is a popular method of model checking for regressions. However, there are two issues that may be the barriers for its powerfulness: existing bias term and curse of dimensionality. The purpose of this paper is thus twofold: a bias reduction is suggested and a dimension reduction‐based adaptive‐to‐model enhancement is recommended to promote the power performance. The proposed test statistic still possesses the Wilks phenomenon and behaves like a test with only one covariate. Thus, it converges to its limit at a much faster rate and is much more sensitive to alternative models than the classical non‐parametric generalized likelihood ratio test. As a by‐product, we also prove that the bias‐corrected test is more efficient than the one without bias reduction in the sense that its asymptotic variance is smaller. Simulation studies and a real data analysis are conducted to evaluate of proposed tests. [ABSTRACT FROM AUTHOR]

In this paper, based on certain residual-marked empirical processes, we study the model test to validate the composite structure with a given link function for parametric single-index time series models. To extend an existing directional test that avoids the curse of dimensionality to an omnibus test, a model-adaptive dimension-reduction test procedure is proposed. Moreover, to fully utilize the dimension-reduction structure under the null hypothesis, the test is designed for adapting both the null and alternative hypotheses, which can improve the power for a more general alternative. Simulation results and a real data example show that the proposed method can perform effectively in checking parametric single-index time series models. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the impacts of joint energy and output prices uncertainties on the input demands in a mean-variance framework. We find that an increase in expected output price will surely cause the risk-averse firm to increase the input demand, while an increase in expected energy price will surely cause the risk-averse firm to decrease the demand for energy, but increase the demand for the non-risky inputs. Furthermore, we investigate the two cases with only uncertain energy price and only uncertain output price. In the case with only uncertain energy price, we find that the uncertain energy price has no impact on the demands for the non-risky inputs. We also show that the concepts of elasticity and decreasing absolute risk aversion (DARA) play an important role in the comparative statics analysis. [ABSTRACT FROM AUTHOR]

This paper first shows the sufficient relationship between the $$(n+1)$$ -order SD and the n-order Kappa ratio. In fact, we clarify the restrictions on necessary beating of the target for the higher-order SD consistency of the Kappa ratios. Thereafter, we show that, in general, the necessary relationship between SD/RSD and the Kappa ratio cannot be established. We find that when the variables being compared belong to the same location-scale family or the same linear combination of location-scale families, we can get the necessary relationship between the $$(n+1)$$ -order SD with the n-order Kappa ratio after imposing some conditions on the means. Our findings enable academics and practitioners to draw better decision in their analysis. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a simple test and an easily applicable modeling procedure for threshold moving average (TMA) models. Firstly, based on the fitted residuals by maximum likelihood estimate (MLE) for MA models, we construct a simple statistic, which is obtained by linear arrange regression and followsF-distribution approximately, to test for threshold nonlinearity and specify the threshold variables. And then, we use some scatterplots to identify the number and locations of the potential thresholds. Finally, with the statistic and Akaike information criterion, we propose the procedure to build TMA models. Both the power of test statistic and the convenience of modeling procedure can work very well demonstrated by simulation experiments and the application to a real example. [ABSTRACT FROM AUTHOR]

Inverse sampling is widely applied in studies with dichotomous outcomes, especially when the subjects arrive sequentially or the response of interest is difficult to obtain. In this paper, we investigate the rate ratio test problem under inverse sampling based on gradient statistic with the asymptotic method and parametric bootstrap technique. The gradient statistic has many advantages, for example, it is simple to calculate and competitive with Wald-type, score and likelihood ratio tests in terms of local power. Numerical studies are carried out to evaluate the performance of our gradient test and the existing tests, namely Wald-type, score and likelihood ratio tests. The simulation results suggest that the gradient test based on the parametric bootstrap method has excellent type I error control and large powers even in small sample design. Two real examples, from a heart disease study and a drug comparison study, are applied to illustrate our methods. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the empirical-likelihood-based inference for the construction of confidence intervals and regions of the parameters of interest in single index models with missing covariates at random. An augmented inverse probability weighted-type empirical likelihood ratio for the parameters of interest is defined such that this ratio is asymptotically standard chi-squared. Our approach is to directly calibrate the empirical log-likelihood ratio, and does not need multiplication by an adjustment factor for the original ratio. Our bias-corrected empirical likelihood is self-scale invariant and no plug-in estimator for the limiting variance is needed. Some simulation studies are carried out to assess the performance of our proposed method. [ABSTRACT FROM AUTHOR]

Birnbaum–Saunders (BS) distribution is widely used in reliability applications to model failure times. For several samples from possible different BS distributions, to prevent wrong conclusions in any further analysis, it is of importance to accompany a formal comparison for characteristic quantities of the distributions, including mean, quantile and reliability function difference. To this end, two test statistics, which are respectively based on the exact generalizedp-value approach and the Delta method, are proposed and their behaviours are investigated. Simulation studies are carried out to examine the size and power performance of the newly proposed statistics. An interesting phenomenon is that in the finite sample simulations we conduct, the Delta method-based test almost uniformly outperforms the generalizedp-value-based test although its sampling null distribution is simulated by Monte Carlo method. This might suggest that the sampling null distribution of the Delta method-based test statistic would have a fast convergence to its limit. The tests are also applied to analyse a real example on the fatigue life of 6061-T6 aluminium coupons for illustration. [ABSTRACT FROM AUTHOR]

Due to the strikingly resemblance to the normal theory and inference methods, the inverse Gaussian (IG) distribution is commonly applied to model positive and right-skewed data. As the shape parameter in the IG distribution is greatly related to other important quantities such as the mean, skewness, kurtosis and the coefficient of variation, it plays an important role in distribution theory. This paper focuses on testing the equality of shape parameters in several inverse Gaussian distributions. Three tests are suggested: the exact generalized inference-based test, the asymptotic test and a test that is based on parametric bootstrap approximation. Simulation studies are undertaken to examine the performances of the these methods, and three real data examples are analyzed for illustration. [ABSTRACT FROM AUTHOR]

In this paper, we use the empirical likelihood method to make inferences for the coefficient difference of a two-sample linear regression model with missing response data. The commonly used empirical likelihood ratio is not concave for this problem, so we append a natural and well-explained condition to the likelihood function and propose three types of restricted empirical likelihood ratios for constructing the confidence region of the parameter in question. It can be demonstrated that all three empirical likelihood ratios have, asymptotically, chi-squared distributions. Simulation studies are carried out to show the effectiveness of the proposed approaches in aspects of coverage probability and interval length. A real data set is analysed with our methods as an example. [ABSTRACT FROM AUTHOR]

In this paper, we develop the concept of almost stochastic dominance for higher order preferences and investigate the related properties of this concept. [ABSTRACT FROM PUBLISHER]

Unfortunately, original article has been published without acknowledgement section. [ABSTRACT FROM AUTHOR]