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Instead of relying on null-hypothesis significance testing (NHST), researchers are consistently advised to use effect sizes (ESs) and confidence intervals (CIs) to convey research findings. However, typical ES measures for most linear models (e.g., multiple regression) assume data normality, a condition that is often violated in behavioral research. This may lead to inaccurate interpretation of ES. In multiple regression models, Cohen's f2, R2 and R adj 2 are employed by researchers, but no study has systematically evaluated their robustness in practice. Thus, this Monte Carlo simulation study evaluates the robustness of f2, R2 and R adj 2 and the associated CIs based on manipulated levels of sample sizes, magnitudes of ESs, numbers of predictors, and data violations (i.e., heavy-tailed, skewed, contaminated, lognormal, and heteroscedastic distributions of errors). This study offers guidelines regarding how robust these ESs are so that researchers can report the most appropriate ES in their research studies. [ABSTRACT FROM AUTHOR]

Small number of clusters combined with cluster level heterogeneity poses a great challenge for the data analysis. We have published a weighted Jackknife approach to address this issue applying weighted cluster means as the basic estimators. The current study proposes a new version of the weighted delete-one-cluster Jackknife analytic framework, which employs Ordinary Least Squares or Generalized Least Squares estimators as the fundamentals. Algorithms for computing estimated variances of the study estimators have also been derived. Wald test statistics can be further obtained, and the statistical comparison in the outcome means of two conditions is determined using the cluster permutation procedure. The simulation studies show that the proposed framework produces estimates with higher precision and improved power for statistical hypothesis testing compared to other methods. [ABSTRACT FROM AUTHOR]

The intraclass correlation coefficient ρ is used as a measure of the degree of resemblance among family members with respect to some biological or psychological attribute, such as blood pressure level or IQ. Many studies have been done in the area. However, a significant gap in the literature is that few statistics are available to test the significance ( H 0 : ρ = 0 against H A : ρ ≠ 0 ) of this important biological measure and no study is available as to the performance of these statistics with respect to level and power. In this paper we develop three large sample test statistics and compare these, in terms of level and power, using extensive simulation studies of these, the Wald test developed in this paper is found to have a very simple form and highest power while maintaining level well. [ABSTRACT FROM AUTHOR]

Data analysis techniques that rely on standard statistical tools and algorithms often encounter problems when dealing with data sets that have large sample sizes. In this study, two statistical tests done in conducting simple linear regression analysis were revisited. In particular, the study simulated the effects of large sample sizes and amount of contamination in the data due to non-sampling errors on the false positive rate of the Kolmogorov-Smirnov (K-S) test in testing for normality of error terms. The study also characterized the effects of varying sample size and amount of contamination in the data on the false negative rate of the t-test in testing the significance of a regression coefficient. Lastly, an optimality index was developed to determine the sample sizes and the values of the percent noise at which both the false positive rate of the K-S test and the false negative rate of the t-test are minimized. [ABSTRACT FROM AUTHOR]

This work proposes powerful tests using bootstrap methods for the tail index in the family of distribution functions with nondegenerate right tail. It is of interest to test whether the right tail of a distribution function is the same as or heavier than that of the Pareto distribution with index m0 for some m0, vs. alternatively the right tail is lighter. Based on the nonparametric tests of Jureckova and Picek (2001, A class of tests on the tail index, Extreme 4:2, 165–183) that use empirical distribution functions of sample maxima, the proposed bootstrap tests employ two-stage subsamplings: One is subsampling from the sample maxima to be applicable even to the heavy tailed index and the other is subsampling from collections of empirical distributions of the sample maxima. We show the first-order validity of the bootstrap for the nonparametric test and construct bootstrap test statistics. The limiting distribution of the bootstrap test statistics in the null hypothesis is established and the consistency of the bootstrap tests is verified. Applications of the proposed bootstrap tests are given to autoregressive time series models. A simulation study is conducted to see performance of the bootstrap tests and it illustrates that the proposed bootstrap tests outperform the existing ones. Real data of heavy tailed financial stock indices are applied to the proposed bootstrap tests. [ABSTRACT FROM AUTHOR]

We propose a kernel density based estimation for nonlinear regression models when neither the response variable nor the covariates can be directly observed, but are measured with multiplicative distortion measurement errors. For the hypothesis testing of parametric components, restricted estimators under the null hypothesis and test statistics are proposed. The asymptotic properties for the estimators and test statistics are established. A bootstrap procedure is proposed to calculate critical values. Lastly, we make some comparisons between the proposed estimator and nonlinear least squares estimator through the simulation. Our simulation studies indicate that the kernel density based estimator provides a better alternative under some settings, particularly for multimodal distribution of the error term. [ABSTRACT FROM AUTHOR]

Multivariate isotonic regression theory plays an important role in the field of testing statistical hypotheses under order restriction for several vector valued parameters and this problem may arise in several situations. Sasabuchi et al. derived a likelihood ratio type test for homogeneity of order restricted mean vectors under a multivariate normality assumption with unknown and common covariance matrices; however, its null distribution depends on the unknown covariance matrix. In order to overcome this difficulty, in the present paper we propose a test that conditions on the sufficient statistic for the covariance matrix. We remove the dependence of the null distribution of test statistic proposed by Sasabuchi et al. on the unknown value of covariance matrix and present a condition on a sufficient statistic for covariance matrix under the null hypothesis. A method based on Markov Chain Monte Carlo sampling is used to calculate the p - value of the test. We evaluate the operating characteristics of the proposed test through simulation studies. Our extensive simulation studies demonstrate that the power of this new test is generally higher than the test proposed by Sasabuchi et al. and also by Sasabuchi. A real data example is presented. [ABSTRACT FROM AUTHOR]

In a high-dimensional linear regression model, this article is concerned with testing statistical significance of a subset of regression coefficients. The conventional partial F-test is not applicable in high-dimensional situations. Several methods for testing whether any of the discarded covariates is significant conditional on relative importance of predictors have been proposed in the recent literature, but they are adversely affected by the overestimation of the variance. To overcome this issue, we propose a novel nonparametric testing procedure to avoid this problem and enhance the empirical power. In addition, the new test is very effective when error distribution deviates from the normal scenario and can integrate all the individual information of the discarded covariates. Under the high-dimensional null and alternative hypotheses, we derive the asymptotic distribution of the proposed test statistic, which allows power evaluation of the test. Numerical studies are carried out to examine the numerical performance of the test. The results show that the test proposed here behaves well in terms of sizes and power and significantly outperforms the existing choices in a range of settings. [ABSTRACT FROM AUTHOR]

We consider the estimation and hypothesis test for single-index model by proposing a profile mean squares relative error estimation method, coupling with a leave-one-component-out method. The asymptotic normality for the PMSRE estimator is studied. For the hypothesis test of the single-index parameter, a restricted estimator under the null hypothesis and test statistics are proposed. The asymptotic properties for the restricted estimator and test statistic are established. Lastly, we make some comparisons between the proposed estimator and the profile least squares estimator and the profile least product relative estimator through the simulation. Our simulation studies indicate that the profile mean squares relative error estimator provides a better alternative under some settings, particularly for multimodal or asymmetric distributions of the error term. [ABSTRACT FROM AUTHOR]

In this article, we mainly discuss hypothesis tests to assess whether nonparametric function satisfies monotonicity or convexity in semiparametric partially linear model. For this purpose, we propose a new test statistic for monotonicity or convexity of nonparametric function based on Bernstein polynomials. Furthermore, we employ likelihood ratio statistic to test the significance of regression parameters of the model. We discuss the asymptotic properties of the tests for both nonparametric function and regression parameters. A simulation study is conducted to evaluate the finite sample performance compared with the other methods for shape tests. The method is illustrated by the fuel efficiency study. [ABSTRACT FROM AUTHOR]

In social sciences, a plethora of studies utilize nominal data to establish the relationship between the variables. This, in turn, requires the correct use of correlation technique. The choice of correlation technique depends upon the underlying assumptions and power of the test of significance. The objective of the research is to explore the best measure of association for nominal data in terms of size, power and bias in estimation. Monte Carlo simulations reveal that the Phi and Pearson correlation statistics performs equally well in terms of size, power, and bias for naturally dichotomous variables. When both variables are artificially dichotomized, the Tetrachoric statistic has an edge in terms of bias to Pearson correlation statistic. If one variable is continuous and other is artificially dichotomized, the Biserial correlation measure turns out to be less biased as compared to Pearson statistic although both statistics exhibit similar power and size properties. If one variable is continuous and other is naturally dichotomized, it is hard to choose between the Point Biserial and Pearson correlation measures. Finally, if one variable is naturally dichotomous and other is artificially dichotomized, correlation coefficient V is compared with Pearson, Phi and Tetrachoric correlation techniques in terms of bias in estimate. The results indicate that the Tetrachoric statistic considerably overestimates the correlation value against non-normal distributions. Pearson and Phi correlation slightly underestimate the correlation value. In contrast, the correlation statistic V perform well. [ABSTRACT FROM AUTHOR]

In this article the optimal statistical hypotheses testing to make decision for arbitrarily varying object are considered. The statistician would like to control the overall error rate relative to draw statistically valid conclusions from each test. The familywise error rate (FWE) metric and the hypothesis test procedure while controlling both the type I and II FWEs are generalized by an arbitrarily varying object. The characteristics of logarithmically asymptotically optimal (LAO) hypotheses testing are studied. The purpose of research is to express the optimal functional relation among the reliabilities of LAO hypotheses testing and to judge with FWE metric. [ABSTRACT FROM AUTHOR]

Suppose we have k samples X 1 , 1 , ... , X 1 , n 1 , ... , X k , 1 , ... , X k , n k with different sample sizes n 1 , ... , n k and unknown underlying distribution functions F 1 , ... , F k as observations plus k families of distribution functions { G 1 (· , ϑ) ; ϑ ∈ Θ } , ... , { G k (· , ϑ) ; ϑ ∈ Θ } , each indexed by elements ϑ from the same parameter set Θ, we consider the new goodness-of-fit problem whether or not (F 1 , ... , F k) belongs to the parametric family { (G 1 (· , ϑ) , ... , G k (· , ϑ)) ; ϑ ∈ Θ }. New test statistics are presented and a parametric bootstrap procedure for the approximation of the unknown null distributions is discussed. Under regularity assumptions, it is proved that the approximation works asymptotically, and the limiting distributions of the test statistics in the null hypothesis case are determined. Simulation studies investigate the quality of the new approach for small and moderate sample sizes. Applications to real-data sets illustrate how the idea can be used for verifying model assumptions. [ABSTRACT FROM AUTHOR]

A semi-parametric spatial-temporal model is estimated using a hybrid of forward search algorithm and nonparametric regression in the context of backfitting an additive model. The model can account for a structural change represented by a function of time. Robust estimates of the parametric component are produced and the predicted model also exhibit good predictive ability. Nonparametric bootstrap is used to facilitate significance test for structural change even without prior knowledge of its nature. The test is properly-sized and powerful as observed from the simulation studies. [ABSTRACT FROM AUTHOR]

In this note we propose a new semi-parametric bootstrap procedure for hypothesis tests about a statistical function and termed bootstrap warping. This procedure was motivated by empirical likelihood and bootstrap tilting techniques. The procedure is computationally efficient and has a fixed number of parameters. We show that the warping procedure has good type I error control and has monotone power as a function of sample size and shift alternatives. [ABSTRACT FROM AUTHOR]

We discuss a two-step approach to test for a mediated effect using data gathered via complex sampling. The approach incorporates design-based multiple linear regressions and a generalized Sobel's method to test for significance of a mediated effect. We illustrate the applications to a study of nicotine dependence, race/ethnicity and cigarette purchase price among daily smokers in the U.S. The study goal was to assess significance of cigarette purchase price as a mediator in the association between race/ethnicity (non-Hispanic Black/African American, non-Hispanic White) and nicotine dependence measured in terms of the average number of cigarettes smoked per day. The single-mediator model incorporated 18 covariates as control factors. The results indicated a significant mediated effect of cigarette purchase price on the association. However, the relative effect size of 5% indicated low practical significance of the cigarette purchase price as a mediator in the association between race/ethnicity and nicotine dependence. The approach can be modified to studies where data are gathered via other types of complex sampling. [ABSTRACT FROM AUTHOR]

Multivariate isotonic regression theory plays a key role in the field of testing statistical hypotheses under order restriction for vector valued parameters. This kind of statistical hypothesis testing has been studied to some extent, for example, by Kulatunga and Sasabuchi (1984) when the covariance matrices are known and also Sasabuchi et al. (2003) and Sasabuchi (2007) when the covariance matrices are unknown but common. In the present paper, we are interested in a general testing for order restriction of mean vectors against all possible alternatives based on a random sample from several p − dimensional normal populations when the unknown covariance matrices are common. In fact, this problem of testing is an extension of Bazyari and Chinipardaz's (2012) problem. We propose an approximate test statistic by likelihood ratio method based on orthogonal projections on the closed convex cones, study its upper tail probability under the null hypothesis and estimate its critical values for different significance levels by using Monte Carlo simulation. The problem of testing and obtained results is illustrated with a real example where this inference problem arises to evaluate the effect of Vinylidene fluoride on liver damage. [ABSTRACT FROM AUTHOR]

The important problem of discriminating between separate families of distributions is the theme of this work. The Bayesian significance test, FBST, is compared with the celebrated Cox test. The three families most used in survival analysis, lognormal, gamma and Weibull, are considered for the discrimination. A convex combination—with unknown weights—of the three densities is used for this discrimination. After these weights have been estimated, the one with the highest value indicates the best statistical model among the three. Another important feature considered is the parameterization used. All the three densities are written as a function of the common population mean and variance. Including the weights, the number of parameters is reduced from eight (two of each density and two of the convex combination) to four (two from the common mean and variance plus two of the weights). Some numerical results from simulations are given. In these simulations, the results of FBST are compared with those obtained with the Cox test. Two real examples properly illustrate the procedures. [ABSTRACT FROM AUTHOR]

There are several statistics in time series either based on sample autocorrelation or partial autocorrelation to test the whiteness hypothesis. In this manuscript we show that departure from normality may mislead the decision making based on the common statistics. The main objective of this article is using the ranks instead of the original data as a non-parametric approach. By an extensive simulation study, we compare the power and significance level of the common statistics and rank-based statistics under different non-normal distributions including heavy-tail and skew-normal distributions. [ABSTRACT FROM AUTHOR]

Smoothed ranks are proposed for two or multi-sample location problems. The regular ranks in Wilcoxon's two sample test are replaced with smoothed ranks, and the shift parameter is estimated. Asymptotic properties of the smoothed rank estimator are shown and a hypothesis test is proposed. Moreover, the smoothed ranks are applied in the Kruskal–Wallis'sr-sample test and the power of the test is computed using regular and smoothed ranks. Examples and Monte Carlo simulations show that the smoothed ranks perform similarly to the traditional rank based estimators under contaminated normal or non-normal populations. [ABSTRACT FROM PUBLISHER]

One of the indicators for evaluating the capability of a process is the process capability index. In this article, bootstrap confidence intervals of the generalized process capability index (GPCI) proposed by Maiti et al. are studied through simulation, when the underlying distributions are Lindley and Power Lindley distributions. The maximum likelihood method is used to estimate the parameters of the models. Three bootstrap confidence intervals namely, standard bootstrap (SB), percentile bootstrap (PB), and bias-corrected percentile bootstrap (BCPB) are considered for obtaining confidence intervals of GPCI. A Monte Carlo simulation has been used to investigate the estimated coverage probabilities and average width of the bootstrap confidence intervals. Simulation results show that the estimated coverage probabilities of the percentile bootstrap confidence interval and the bias-corrected percentile bootstrap confidence interval get closer to the nominal confidence level than those of the standard bootstrap confidence interval. Finally, three real datasets are analyzed for illustrative purposes. [ABSTRACT FROM AUTHOR]

This article considers multiple hypotheses testing with the generalized familywise error ratek-FWER control, which is the probability of at leastkfalse rejections. We first assume thep-values corresponding to the true null hypotheses are independent, and propose adaptive generalized Bonferroni procedure withk-FWER control based on the estimation of the number of true null hypotheses. Then, we assume thep-values are dependent, satisfying block dependence, and propose adaptive procedure withk-FWER control. Extensive simulations compare the performance of the adaptive procedures with different estimators. [ABSTRACT FROM AUTHOR]

In this study, we considered a hypothesis test for the difference of two population means using ranked set sampling. We proposed a test statistic for this hypothesis test with more than one cycle under normality. We also investigate the performance of this test statistic, when the assumptions hold and are violated. For this reason, we investigate the type I error and power rates of tests under normality with equal and unequal variances, non-normality with equal and unequal variances. We also examine the performance of this test under imperfect ranking case. The simulation results show that derived test performs quite well. [ABSTRACT FROM AUTHOR]

A well-designed clinical trial requires an appropriate sample size with adequate statistical power to address trial objectives. The statistical power is traditionally defined as the probability of rejecting the null hypothesis with a pre-specified true clinical treatment effect. This power is a conditional probability conditioned on the true but actually unknown effect. In practice, however, this true effect is never a fixed value. Thus, we discuss a newly proposed alternative to this conventional statistical power:statistical assurance, defined as the unconditional probability of rejecting the null hypothesis. This kind of assurance can then be obtained as an expected power where the expectation is based on the prior probability distribution of the unknown treatment effect, which leads to the Bayesian paradigm. In this article, we outline the transition from conventional statistical power to the newly developed assurance and discuss the computations of assurance using Monte Carlo simulation-based approach. [ABSTRACT FROM AUTHOR]

There are many hypothesis testing settings in which one can calculate a "reasonable" test statistic, but in which the null distribution of the statistic is unknown or completely intractable. Fortunately, in many such situations, it is possible to simulate values of the test statistic under the null hypothesis, in which case one can conduct a Monte Carlo test. A difficulty however arises in that Monte Carlo tests, as they are currently structured, are applicable only if ties cannot occur among the values of the test statistics. There is a frequently occurring scenario in which there are lots of ties, namely that in which the null distribution of the test statistic has a (single) point mass. It turns out that one can modify the current form of Monte Carlo tests so as to accommodate such settings. Developing this modification leads to an intriguing identity involving the binomial probability function and its derivatives. In this article, we will briefly explain the modified procedure, discuss simulation studies which demonstrate its efficacy, and provide a proof of the identity referred to above. [ABSTRACT FROM AUTHOR]

High-frequency trading activities are one of the common phenomena in nowadays financial markets. Enormous amounts of high-frequency trading data are generated by huge numbers of market participants in every trading day. The availability of this information allows researchers to further examine the statistical properties of informationally efficient market hypothesis (EMH). Heterogenous market hypothesis (HMH) is one of the important extensions ofEMHliterature.HMHintroduced nonlinear trading behaviors of heterogenous market participants instead of normality assumption under the EMH homogenous market participants. In this study, we attempt to explore more high-frequency volatility estimators in the HMH examination. These include the bipower, tripower, and quadpower variation integrated volatility estimates using Heterogenous AutoRegressive (HAR) models. The empirical findings showthat these alternatives multipower variation (MPV) estimators provide better estimation and out-of-sample forecast evaluations as compared to the standard realized volatility. In other words, the usage of MPV estimators is able to better explain the HMH statistically. At last, a market risk determination is illustrated using value-at-risk approach. [ABSTRACT FROM AUTHOR]

The log-Birnbaum-Saunders regression model introduced by Rieck and Nedelman (1991) is useful for modeling lifetimes of materials and equipments subject to different conditions. Our goal in this article is twofold. First, we numerically evaluate the finite sample performances of the likelihood ratio, score and Wald tests in the log-Birnbaum-Saunders regression model. Second, we introduce a RESET-like misspecification test for that model. The null hypothesis is that the model is correctly specified which is tested against the alternative hypothesis of model misspecification. The power of the test is evaluated using Monte Carlo simulations. Bootstrap-based inference is also considered. An empirical application is presented and discussed. [ABSTRACT FROM AUTHOR]

We propose a new meta-analysis method to pool univariatep-values across independent studies and we compare our method with that of Fisher, Stouffer, and George through simulations and identify sub-spaces where each of these methods are optimal and propose a strategy to choose the best meta-analysis method under different sub-spaces. We compare these meta-analysis approaches usingp-values from periodicity tests of 4,940S. Pombegenes from 10 independent time-course experiments and show that our new approach ranks the periodic, conserved, and cycling genes much higher, and detects at least as many genes among the top 1,000 genes, compared to other methods. [ABSTRACT FROM PUBLISHER]

One standard summary of a clinical trial is a confidence limit for the effect of the treatment. Unfortunately, standard approximate limitsmay have poor frequentist properties, even for quite large sample sizes. It has been known since Buehler (1957) that an imperfect confidence limit can be adjusted to have exact coverage. These "tight" limits are the gold standard frequentist confidence limit. Computing tight limits requires exact calculation of certain tail probabilities and optimisation of potentially erratic functions of the nuisance parameter. Naive implementation is both computationally unreliable and highly burdensome, and perhaps explains why they are not in common use. For clinical trials however, where the data and parameter have dimension two, the difficulties can be fully surmounted. This paper brings together several results in the area and applies them to simple two dimensional problems. It is shown how to reduce the computational burden by an order of magnitude. Difficulties with the optimisation reliability are mitigated by applying two different computational strategies,which tend to break down under different conditions, and taking the less stringent of the two computed limits. This paper specifically develops limits for the relative risk in a clinical trial, but it should be clear to the reader that the method extends to arbitrary measures of treatment effect without essential modification. [ABSTRACT FROM AUTHOR]

We propose an algorithm of multiple comparisons in two-factor testing of psychophysical thresholds. We use logistic regression combined with guessing rate and adopt the step-down procedure with Ryan-Einot-Gabriel-Welsch (REGW) significance levels for multiple test. We test hypotheses of main effects and interaction in two-factor problem by using the delta test statistics. [ABSTRACT FROM AUTHOR]

This study aims to provide a reliable confidence interval for assessing the process incapability index [Cpp]. The concept of the generalized pivotal quantities is utilized for constructing the generalized confidence interval for [Cpp]. And, simulations are performed for demonstrating our proposed method and one existent method. The results show that the empirical confidences of these two methods are significantly affected by the degree of process departure. Therefore, we suggest the practitioners to select proper one for capability testing purpose based on the information of degree of process departure. [ABSTRACT FROM AUTHOR]

This article is concernedwith comparison of a fewrandomvariate generation techniques for the generalized Poisson distribution. An evaluation is conducted on the degree of proximity between the estimates for its two distributional parameters and first four moments, and the specified or computed true population values via commonly accepted accuracy and precision measures in a simulated setting. [ABSTRACT FROM AUTHOR]

In this study, we consider the construction of the confidence interval for the population proportion while using group testing with misclassification. We propose two confidence intervals based on Cornish-Fisher expansion and a modified Wilson's interval based on a newly developed estimator. We investigate the performance of these intervals extensively and also apply the methods to real datasets. Our newly derived methods have competitive performance compared with existing methods. [ABSTRACT FROM AUTHOR]

In the estimation of a proportion p by group testing (pooled testing), retesting of units within positive groups has received little attention due to the minimal gain in precision compared to testing additional units. If acquisition of additional units is impractical or too expensive, and testing is not destructive, we show that retesting can be a useful option. We propose the retesting of a random grouping of units from positive groups, and compare it with nested halving procedures suggested by others. We develop an estimator of p for our proposed method, and examine its variance properties. Using simulation we compare retesting methods across a range of group testing situations, and show that for most realistic scenarios, our method is more efficient. [ABSTRACT FROM AUTHOR]

Box–Cox power transformation is a commonly used methodology to transform the distribution of the data into a normal distribution. The methodology relies on a single transformation parameter. In this study, we focus on the estimation of this parameter. For this purpose, we employ seven popular goodness-of-fit tests for normality, namely Shapiro–Wilk, Anderson–Darling, Cramer-von Mises, Pearson Chi-square, Shapiro-Francia, Lilliefors and Jarque–Bera tests, together with a searching algorithm. The searching algorithm is based on finding the argument of the minimum or maximum depending on the test, i.e., maximum for the Shapiro–Wilk and Shapiro–Francia, minimum for the rest. The artificial covariate method of Dag et al. (2014) is also included for comparison purposes. Simulation studies are implemented to compare the performances of the methods. Results show that Shapiro–Wilk and the artificial covariate method are more effective than the others and Pearson Chi-square is the worst performing method. The methods are also applied to two real-life datasets. The R package AID is proposed for implementation of the aforementioned methods. [ABSTRACT FROM AUTHOR]

The case that the factor model does not account for all the covariances of the observed variables is considered. It is shown that principal components representing covariances not accounted for by the factor model can have a nonzero correlation with the common factors of the factor model. The substantial correlations of components representing variance not accounted for by the factor model with common factors are demonstrated in a simulation study comprising model error. Based on these results, a new version of Harman's factor score predictor minimizing the correlation with residual components is proposed. [ABSTRACT FROM AUTHOR]

We propose a procedure to identify a lowest dose having greater effect than a threshold dose under the assumption of monotonicity of dose mean response in dose response test. So, we use statistics based on contrasts among sample means and apply a group sequential procedure to our procedure to identify effectively the dose. If we can identify the dose at an early step in the sequential test, since we can terminate the procedure with a few observations, the procedure is useful from an economical point of view. In a simulation studies, we compare the superiority among these procedures based on three contrasts. [ABSTRACT FROM AUTHOR]

This article proposes a new fractional age assumption (FAA) based on the cubic polynomial interpolation (CPI) and applies it to estimate the mortality rate and related actuarial quantities. The validity of the method under CPI is proved theoretically and the valuable advantages of CPI assumption are discussed based on three different perspectives—utilized death information, property of mortality force, and optimality criterion. The results show that CPI assumption has distinct valuable superiority compared with other FAAs in references. Finally under CPI assumption we study the calculations of some important actuarial quantities in life contingencies. [ABSTRACT FROM AUTHOR]

The Birnbaum–Saunders distribution is a positively skewed distribution that is frequently used for analyzing lifetime data. Regression analysis is widely used in this context when some covariates are involved in the life-test. In this article, we discuss the maximum likelihood estimation of the model parameters and associated inference. We discuss the likelihood-ratio tests for some hypotheses of interest as well as some interval estimation methods. A Monte Carlo simulation study is then carried out to examine the performance of the proposed estimators and the interval estimation methods. Finally, some numerical data analyses are done for illustrating all the inferential methods developed here. [ABSTRACT FROM PUBLISHER]

Structural breaks in the level as well as in the volatility have often been exhibited in economic time series. In this paper, we propose new unit root tests when a time series has multiple shifts in its level and the corresponding volatility. The proposed tests are Lagrangian multiplier type tests based on the residual's marginal likelihood which is free from the nuisance mean parameters. The limiting null distributions of the proposed tests are the χ2distributions, and are affected not by the size and the location of breaks but only by the number of breaks. We set the structural breaks under both the null and the alternative hypotheses to relieve a possible vagueness in interpreting test results in empirical work. The null hypothesis implies a unit root process with level shifts and the alternative connotes a stationary process with level shifts. The Monte Carlo simulation shows that our tests are locally more powerful than the OLSE-based tests, and that the powers of our tests, in a fixed time span, remain stable regardless the number of breaks. In our application, we employ the data which are analyzed by Perron (1990), and some results differ from those of Perron's (1990). [ABSTRACT FROM AUTHOR]

Test statistics from the class of two-sample linear rank tests are commonly used to compare a treatment group with a control group. Two independent random samples of sizes m and n are drawn from two populations. As a result, N = m + n observations in total are obtained. The aim is to test the null hypothesis of identical distributions. The alternative hypothesis is that the populations are of the same form but with a different measure of central tendency. This article examines mid p-values from the null permutation distributions of tests based on the class of two-sample linear rank statistics. The results obtained indicate that normal approximation-based computations are very close to the permutation simulations, and they provide p-values that are close to the exact mid p-values for all practical purposes. [ABSTRACT FROM AUTHOR]