Nominal Significance Level Research Articles

A MR-PheWAS and bidirectional Mendelian randomization study: Exploring for causal relationships of pancreatic cancer.

It is unknown what causes pancreatic cancer. We conducted a phenome-wide Mendelian randomization analysis (MR-pheWAS), a bidirectional Mendelian study, and a systematic review of research in order to thoroughly investigate any causal association between pancreatic cancer and Atlas. We used phenome-wide Mendelian randomization analysis to test for associations between pancreatic cancer and 776 phenotypes (n = 452,264) of Atlas in the UK Biobank. Causality is confirmed by two-sample Mendelian randomization (MR) analysis using correlation found by false discovery rate correction. Simultaneously, a comprehensive evaluation of pancreatic cancer MR studies was conducted in order to complement our findings and harmonize the existing evidence. According to the inverse-variance-weighted model, a total of 41 out of 776 phenotypes had a nominal significance level (P < .05) genetic prediction association with pancreatic cancer. Only genetically predicted pancreatic cancer was shown to be linked with elevated eosinophil counts following false discovery rate correction (P = .031) when several tests were taken into account. Pancreatic cancer and eosinophils were shown to be positively causally associated to one another, establishing a self-loop, according to two-sample MR validation in the IEU database (OR = 1.011, 95% CI: 1.002-1.020, P = .010) (OR = 1.229, 95% CI: 1.037-1.458, P = .017). Although MR-pheWAS found a strong causal relationship between eosinophils and pancreatic cancer, it also found a negative exclusion value for each phenotype and a significant number of suggestive association phenotypes that offered guidance for further research.

A metabolomic profile of biological aging in 250,341 individuals from the UK Biobank

The metabolomic profile of aging is complex. Here, we analyse 325 nuclear magnetic resonance (NMR) biomarkers from 250,341 UK Biobank participants, identifying 54 representative aging-related biomarkers associated with all-cause mortality. We conduct genome-wide association studies (GWAS) for these 325 biomarkers using whole-genome sequencing (WGS) data from 95,372 individuals and perform multivariable Mendelian randomization (MVMR) analyses, discovering 439 candidate “biomarker - disease” causal pairs at the nominal significance level. We develop a metabolomic aging score that outperforms other aging metrics in predicting short-term mortality risk and exhibits strong potential for discriminating aging-accelerated populations and improving disease risk prediction. A longitudinal analysis of 13,263 individuals enables us to calculate a metabolomic aging rate which provides more refined aging assessments and to identify candidate anti-aging and pro-aging NMR biomarkers. Taken together, our study has presented a comprehensive aging-related metabolomic profile and highlighted its potential for personalized aging monitoring and early disease intervention.

Multivariate normality tests with two-step monotone missing data: a critical review with emphasis on the different methods of handling missing values

Two-step monotone missing data is a special case of missing data that attracted the attention of researchers since it not only appears frequently in applications but also since under the hypothesis of multivariate normality a unique and explicit solution to the likelihood equations exists. This importance prompted Yamada et al. [Kurtosis tests for multivariate normality with monotone incomplete data. Test. 2015;24:532–557. doi: 10.1007/s11749-014-0423-1] and Kurita and Seo [Multivariate normality test based on kurtosis with two-step monotone missing data. J Multivar Anal. 2022;188:104–824. doi: 10.1016/j.jmva.2021.104824] to propose four procedures in total for testing multivariate normality with two-step monotone missing data. The aim of this paper is twofold. On the one, to compare, for the first time, the performance of the four aforementioned multivariate normality tests via a Monte Carlo study. The second aim of the paper is to compare the performance of the four existing testing procedures with the respective procedures derived as a combination of 12 popular methods for handling incomplete data and 6 multivariate normality tests for complete data. We show that in several cases the tests proposed by Yamada et al. [Kurtosis tests for multivariate normality with monotone incomplete data. Test. 2015;24:532–557. doi: 10.1007/s11749-014-0423-1] and Kurita and Seo [Multivariate normality test based on kurtosis with two-step monotone missing data. J Multivar Anal. 2022;188:104–824. doi: 10.1016/j.jmva.2021.104824] are unable to achieve the nominal significance level. At the same time, no single procedure was found to be the most powerful in all situations of multivariate alternatives considered. As a consequence, the use of specific combinations of methods of handling missing data and testing multivariate normality with complete data is recommended.

Exploring the Bidirectional Effects of Gut Microbiota and Short-Chain Fatty Acids on Urticaria Subtypes Through Mendelian Randomization and Mediation Analysis.

Emerging evidence links gut microbiota and their by-products, notably short-chain fatty acids (SCFAs), to urticaria. This study employs multiple Mendelian Randomization (MR) analyses to unravel the complex interactions among gut microbiota, SCFAs, and different subtypes of urticaria, aiming to elucidate the underlying mechanisms and enhance future clinical research. We analyzed published genome-wide association study (GWAS) summary statistics to identify associations between gut microbiota and three common subtypes of urticaria: spontaneous, dermatographic, and temperature-triggered. Initial two-sample and reverse MR analyses explored the causality in these relationships. Subsequent multivariate MR analyses investigated the role of SCFAs in modulating these interactions, with multiple sensitivity analyses to ensure robustness. Specific taxa were differently associated with various urticaria subtypes. From microbiota to urticaria: one taxon was negatively associated with dermatographic urticaria; seven taxa were negatively associated and four positively associated with temperature-triggered urticaria; four taxa were negatively associated and six positively associated with spontaneous urticaria. Conversely, from urticaria to microbiota: five taxa were negatively associated with dermatographic urticaria; four were negatively and two positively associated with temperature-triggered urticaria; and two were negatively associated with spontaneous urticaria. These associations were observed at a nominal significance level (P < 0.05). After applying Bonferroni correction for multiple testing, these associations did not reach statistical significance. The observed trends, however, provide insights into potential microbiota-urticaria interactions. Multivariate MR analyses elucidated the role of SCFAs, particularly acetate, which plays a crucial role in modulating immune response. Adjusting for acetate revealed direct effects of Actinobacteria, Bifidobacteriales, and Bifidobacteriaceae on spontaneous urticaria, with corresponding mediation effects of -22%, -24.9%, and -24.9% respectively. Similarly, adjustments for Alcaligenaceae and Betaproteobacteria indicated significant negative effects of acetate on dermatographic and spontaneous urticaria, with mediation effects of -21.7% and -23.7%, respectively. This study confirms the interconnected roles of gut microbiota, SCFAs, and urticaria. It highlights SCFAs' potential mediating role in influencing urticaria through microbiota, providing insights for future therapeutic strategies.

Survey expectations and adjustments for multiple testing

Testing hypotheses regarding how individual survey respondents form their expectations is susceptible to the multiple testing problem. The probability of falsely rejecting the null hypothesis for one or more respondents will exceed the nominal single-hypothesis significance level. The Bonferroni correction and related approaches control the family-wise error rate, but are conservative and result in low power when the null hypotheses are false.We compare controlling the family-wise error rate with the effect of controlling the false discovery rate and the false discovery proportion, in terms of the conclusions we draw about forecaster behaviour.The effects of adjustments for multiple testing are investigated for tests of weak efficiency and the over-reaction hypothesis, for beliefs about the persistence of shocks to output growth, and for the accuracy of survey respondents’ perceptions of the uncertainty they face.

Nonparametric Testing of the Covariate Significance for Spatial Point Patterns under the Presence of Nuisance Covariates

Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model of interactions between points is wrong. Therefore, we propose a fully nonparametric approach to testing significance of a covariate, taking into account the possible effects of nuisance covariates. Our tests match the nominal significance level, and their powers are comparable with the powers of parametric tests in cases where both the model for intensity function and the model for interactions are correct. When the parametric model for the intensity function is wrong, our tests achieve higher powers. The proposed methods rely on Monte Carlo testing and take advantage of the newly introduced concepts: the covariate-weighted residual measure and nonparametric residuals. We also define a correlation coefficient between a point process and a covariate and a partial correlation coefficient quantifying the dependence between a point process and a covariate of interest while removing the influence of nuisance covariates. Supplementary materials for this article are available online.

An empirical study of the durbin and ANOVA F tests and their contrasts

The Durbin and ANOVA F test on the ranks are competing tests for equality of treatment mean ranks. When the levels of the treatments are ordered, both tests have informative decompositions into orthonormal contrasts that detect linear, quadratic, etc. effects. It is known that the ANOVA F test on the ranks better adheres to the nominal significance level than the Durbin test. Here we investigate whether the same is true for their orthonormal contrasts.

Identity-by-descent segments in large samples.

We show the asymptotic normality of the identity-by-descent rate, a mean of correlated binary random variables that arises in population genetics studies.We describe an efficient algorithm capable of simulating long identity-by-descent segments around a locus in large sample sizes.In enormous simulation studies, we use this algorithm to characterize the distributional properties of the identity-by-descent rate.In finite samples, we reject the null hypothesis of normality more often than the nominal significance level, indicating that genome-wide scans based on identity-by-descent rates may be anti-conservative.

No role of the third-trimester inflammatory factors in the association of gestational diabetes mellitus with postpartum cardiometabolic indicators

BackgroundThe influence of gestational diabetes mellitus (GDM) on postpartum cardiometabolic indicators is primarily restricted to glucose and lipid metabolism, however the indicators for liver and kidney function have been rarely explored, and the role of the third-trimester inflammatory factors in these associations has never been investigated.MethodsBased on the Ma’anshan birth cohort (MABC), women with or without GDM history were selected and invited to participate in a 6-year postpartum follow-up. The fasting blood samples were collected to measure 16 comprehensive metabolic indicators during a 6-year postpartum follow-up: fasting plasma glucose (FPG), glycosylated hemoglobin (HbA1c), triglycerides (TG), total cholesterol (TC), uric acid (UA), blood urea nitrogen (BUN), serum creatinine (SCR), etc. Seven inflammatory factors, including TNF-α, IFN-γ, IL-1β, IL-6, IL-10, IL-12p70, and IL-17 A, were measured with serum samples collected during the third trimester of pregnancy. Linear regression models were used to analyze the associations between GDM and 6-year postpartum metabolic indicators, GDM and third-trimester inflammatory factors, and the third-trimester inflammatory factors and 6-year postpartum metabolic indicators. Mediating and moderating effect analyses were further performed to explore if the third-trimester inflammatory factors mediate or modify the association between GDM and postpartum cardiometabolic indicators.ResultsFrom July 2021 to August 2022, 307 participants have been followed up, with 99 women with a prior GDM history. Compared with those without GDM, individuals with a prior history of GDM had significantly elevated levels of FPG (β = 0.40, 95% CI: 0.18 to 0.62, PFDR < 0.001), HbA1c (β = 0.22, 95% CI: 0.09 to 0.34, PFDR = 0.009), TyG (β = 0.22, 95% CI: 0.07 to 0.37, PFDR = 0.024) at 6 years postpartum, and the association between GDM and SCR (β = 2.43, 95% CI: 0.02 to 4.85, PFDR = 0.144) reached nominal significance level. GDM history was associated with a decreased level of third-trimester IL-17 A (β = -0.58, 95% CI: -0.99 to -0.18, PFDR = 0.035). No significant association between third-trimester inflammatory factors and 6-year postpartum metabolic indicators was observed. And no mediating or moderating effect of third-trimester inflammatory factors was observed in those associations.ConclusionA prior history of GDM was significantly associated with elevated FPG, HbA1c, and TyG in women at 6 years postpartum, whereas third-trimester inflammatory factors had no role in mediating or moderating these associations.

Risk Factors for Intracerebral Hemorrhage: Genome-Wide Association Study and Mendelian Randomization Analyses.

The genetic and nongenetic causes of intracerebral hemorrhage (ICH) remain obscure. The present study aimed to uncover the genetic and modifiable risk factors for ICH. We meta-analyzed genome-wide association study data from 3 European biobanks, involving 7605 ICH cases and 711 818 noncases, to identify the genomic loci linked to ICH. To uncover the potential causal associations of cardiometabolic and lifestyle factors with ICH, we performed Mendelian randomization analyses using genetic instruments identified in previous genome-wide association studies of the exposures and ICH data from the present genome-wide association study meta-analysis. We performed multivariable Mendelian randomization analyses to examine the independent associations of the identified risk factors with ICH and evaluate potential mediating pathways. We identified 1 ICH risk locus, located at the APOE genomic region. The lead variant in this locus was rs429358 (chr19:45411941), which was associated with an odds ratio of ICH of 1.17 (95% CI, 1.11-1.20; P=6.01×10-11) per C allele. Genetically predicted higher levels of body mass index, visceral adiposity, diastolic blood pressure, systolic blood pressure, and lifetime smoking index, as well as genetic liability to type 2 diabetes, were associated with higher odds of ICH after multiple testing corrections. Additionally, a genetic increase in waist-to-hip ratio and liability to smoking initiation were consistently associated with ICH, albeit at the nominal significance level (P<0.05). Multivariable Mendelian randomization analysis showed that the association between body mass index and ICH was attenuated on adjustment for type 2 diabetes and further that type 2 diabetes may be a mediator of the body mass index-ICH relationship. Our findings indicate that the APOE locus contributes to ICH genetic susceptibility in European populations. Excess adiposity, elevated blood pressure, type 2 diabetes, and smoking were identified as the chief modifiable cardiometabolic and lifestyle factors for ICH.

Multichannel detection of evoked responses using critical values corrected by a parametric bootstrap: Frequency-domain cholesky correction

Multivariate Objective Response Detection (MORD) techniques aim to detect evoked responses in multichannel electroencephalographic (EEG) recordings. They provide enhanced statistical power, allowing the detection of small signals in shorter or noisy recordings. However, the correlation between the signals in multichannel recordings can lead to false positive rates greater than the nominal significance level of the tests. To address this, we propose a parametric bootstrap approach that adjusts the critical values based on the correlation between EEG channels in the time domain, a method called time-domain Cholesky correction (TDCC). In that first approach, we assumed that correlation (or, more precisely, coherence) is constant across all frequency bands. However, this is unlikely to hold true, as signal-to-noise ratios (where the signal is the evoked response and noise all other signal components) may vary across frequencies. Thus, in the current work, we propose an alternative parametric bootstrap method for estimating the critical values of MORD techniques based on the correlation in the frequency domain (FDCC, frequency-domain Cholesky-corrected critical values). The proposed methods are evaluated using simulated data and an auditory steady-state response (ASSR) database in the 40 Hz range. The proposed method controlled the false positive rate well, with increased sensitivity compared to single-channel methods. When compared to TDCC, FDCC achieved similar performance but with the advantage of being, on average, 27 times faster in terms of computational time required to estimate the critical values.

Optimal futility stopping boundaries for binary endpoints

BackgroundGroup sequential designs incorporating the option to stop for futility at the time point of an interim analysis can save time and resources. Thereby, the choice of the futility boundary importantly impacts the design’s resulting performance characteristics, including the power and probability to correctly or wrongly stop for futility. Several authors contributed to the topic of selecting good futility boundaries. For binary endpoints, Simon’s designs (Control Clin Trials 10:1–10, 1989) are commonly used two-stage designs for single-arm phase II studies incorporating futility stopping. However, Simon’s optimal design frequently yields an undesirably high probability of falsely declaring futility after the first stage, and in Simon’s minimax design often a high proportion of the planned sample size is already evaluated at the interim analysis leaving only limited benefit in case of an early stop.MethodsThis work focuses on the optimality criteria introduced by Schüler et al. (BMC Med Res Methodol 17:119, 2017) and extends their approach to binary endpoints in single-arm phase II studies. An algorithm for deriving optimized futility boundaries is introduced, and the performance of study designs implementing this concept of optimal futility boundaries is compared to the common Simon’s minimax and optimal designs, as well as modified versions of these designs by Kim et al. (Oncotarget 10:4255–61, 2019).ResultsThe introduced optimized futility boundaries aim to maximize the probability of correctly stopping for futility in case of small or opposite effects while also setting constraints on the time point of the interim analysis, the power loss, and the probability of stopping the study wrongly, i.e. stopping the study even though the treatment effect shows promise. Overall, the operating characteristics, such as maximum sample size and expected sample size, are comparable to those of the classical and modified Simon’s designs and sometimes better. Unlike Simon’s designs, which have binding stopping rules, the optimized futility boundaries proposed here are not adjusted to exhaust the full targeted nominal significance level and are thus still valid for non-binding applications.ConclusionsThe choice of the futility boundary and the time point of the interim analysis have a major impact on the properties of the study design. Therefore, they should be thoroughly investigated at the planning stage. The introduced method of selecting optimal futility boundaries provides a more flexible alternative to Simon’s designs with non-binding stopping rules. The probability of wrongly stopping for futility is minimized and the optimized futility boundaries don’t exhibit the unfavorable properties of an undesirably high probability of falsely declaring futility or a high proportion of the planned sample evaluated at the interim time point.

ANOVA F Test of Non-Null Hypothesis

ANOVA, a test of a null hypothesis, is limited in assessing the statistical significance of differences. This paper considers an ANOVA F test of the non-null hypothesis for comparing k group means. A margin is chosen for the difference of means between each group and the kth group. A non-null hypothesis is defined to be the difference equal to the margin instead of zero. Data are thus prepared under the non-null hypothesis. Then follows the derivation of the one-way ANOVA non-null F test and its power. It reduces to the classical F test on setting the margin equal to zero. The observed size of it is identical to that of the F test and is near the nominal level of significance. The observed power is close to the power in balanced designs. With the non-null F test, it enables inferences to extend to the equivalence of group means or the clinical significance of differences. An example is taken to analyze both non-inferiority trials and k-sample equivalence trials.

Comparison of baseline covariate adjustment methods for restricted mean survival time

The restricted mean survival time provides a straightforward clinical measure that dispenses with the need for proportional hazards assumptions. We focus on two strategies to directly model the survival time and adjust covariates. Firstly, pseudo-survival time is calculated for each subject using a leave-one-out approach, followed by a model analysis that adjusts for covariates using all pseudo-values. This method is used to reflect information of censored subjects in the model analysis. The second approach adjusts for covariates for those subjects with observed time-to-event while incorporating censored subjects using inverse probability of censoring weighting (IPCW). This paper evaluates these methods' power to detect group differences through computer simulations. We find the interpretation of pseudo-values challenging with the pseudo-survival time method and confirm that pseudo-survival times deviate from actual data in a primary biliary cholangitis clinical trial, mainly due to extensive censoring. Simulations reveal that the IPCW method is more robust, unaffected by the balance of censors, whereas pseudo-survival time is influenced by this balance. The IPCW method retains a nominal significance level for the type-1 error rate, even amidst group differences concerning censor incidence rates and covariates. Our study concludes that IPCW and pseudo-survival time methods differ significantly in handling censored data, impacting parameter estimations. Our findings suggest that the IPCW method provides more robust results than pseudo-survival time and is recommended, even when censor probabilities vary between treatment groups. However, pseudo-survival time remains a suitable choice when censoring probabilities are balanced.

Comparing network structures on three aspects: A permutation test.

Network approaches to psychometric constructs, in which constructs are modeled in terms of interactions between their constituent factors, have rapidly gained popularity in psychology. Applications of such network approaches to various psychological constructs have recently moved from a descriptive stance, in which the goal is to estimate the network structure that pertains to a construct, to a more comparative stance, in which the goal is to compare network structures across populations. However, the statistical tools to do so are lacking. In this article, we present the network comparison test (NCT), which uses resampling-based permutation testing to compare network structures from two independent, cross-sectional data sets on invariance of (a) network structure, (b) edge (connection) strength, and (c) global strength. Performance of NCT is evaluated in simulations that show NCT to perform well in various circumstances for all three tests: The Type I error rate is close to the nominal significance level, and power proves sufficiently high if sample size and difference between networks are substantial. We illustrate NCT by comparing depression symptom networks of males and females. Possible extensions of NCT are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A simple recommendation for the analysis of matching data.

The matching paradigm can take a number of forms and has been used in many areas of psychology. When participants are asked to match or order sets of objects, researchers must correctly account for the number of matches expected purely by chance. Not accounting for the expected chance matches can lead to incorrectly drawing conclusions based on one's data. This study demonstrated that the z test can be an appropriate and easy test to use in the analysis of matching data from studies that require pairs of objects to be matched with each other. This article proves that in a matching paradigm the expected number of chance matches is 1.0 and the associated variance is also 1.0. The z test is shown to maintain the Type I error close to the nominal significance level when the null hypothesis is true and the sample size is at least 80 or 110. To attain power of .80, a sample size larger than 80 may be needed depending upon the effect size associated with the area of interest and the hypothesized alternative probability distribution. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Testing the annual rainfall dispersion in Chaiyaphum, Thailand, by using confidence intervals for the coefficient of variation of an inverse gamma distribution

In Thailand, droughts are regular natural disasters that happen nearly every year due to several factors such as precipitation deficiency, human activity, and the global warming. Since annual rainfall amount fits an inverse gamma (IG) distribution, we wanted to try testing annual rainfall dispersion via the coefficient of variation (CV). Herein, we propose two statistics for testing the CV of an IG distribution based on the Score and Wald methods. We evaluated their performances by means of the Monte Carlo simulations conducted under several shape parameter values for an IG distribution based on empirical type I error rates and powers of the tests. The simulation results reveal that the Wald-method test statistic performed better than the Score-method one in terms of the attained nominal significance level, and is thus recommended for analysis in similar scenarios. Furthermore, the efficacy of the proposed test statistics was illustrated by applying them to the annual rainfall amounts in Chaiyaphum, Thailand.

DNA methylation and 28-year cardiovascular disease risk in type 1 diabetes: the Epidemiology of Diabetes Complications (EDC) cohort study

BackgroundThe potential for DNA methylation (DNAm) as an early marker for cardiovascular disease (CVD) and how such an association might differ by glycemic exposure has not been examined in type 1 diabetes, a population at increased CVD risk. We thus performed a prospective epigenome-wide association study of blood leukocyte DNAm (EPIC array) and time to CVD incidence over 28 years in a childhood-onset (< 17 years) type 1 diabetes cohort, the Pittsburgh Epidemiology of Diabetes Complications (EDC) study (n = 368 with DNA and no CVD at baseline), both overall and separately by glycemic exposure, as measured by HbA1c at baseline (split at the median: < 8.9% and ≥ 8.9%). We also assessed whether DNAm-CVD associations were independent of established cardiometabolic risk factors, including body mass index, estimated glucose disposal rate, cholesterol, triglycerides, blood pressure, pulse rate, albumin excretion rate, and estimated glomerular filtration rate.ResultsCVD (first instance of CVD death, myocardial infarction, coronary revascularization, ischemic ECG, angina, or stroke) developed in 172 participants (46.7%) over 28 years. Overall, in Cox regression models for time to CVD, none of the 683,597 CpGs examined reached significance at a false discovery rate (FDR) ≤ 0.05. In participants with HbA1c < 8.9% (n = 180), again none reached FDR ≤ 0.05, but three were associated at the a priori nominal significance level FDR ≤ 0.10: cg07147033 in MIB2, cg12324048 (intergenic, chromosome 3), and cg15883830 (intergenic, chromosome 1). In participants with HbA1c ≥ 8.9% (n = 188), two CpGs in loci involved in calcium channel activity were significantly associated with CVD (FDR ≤ 0.05): cg21823999 in GPM6A and cg23621817 in CHRNA9; four additional CpGs were nominally associated (FDR ≤ 0.10). In participants with HbA1c ≥ 8.9%, DNAm-CVD associations were only modestly attenuated after cardiometabolic risk factor adjustment, while attenuation was greater in those with HbA1c < 8.9%. No pathways were enriched in those with HbA1c < 8.9%, while pathways for calcium channel activity and integral component of synaptic membrane were significantly enriched in those with HbA1c ≥ 8.9%.ConclusionsThese results provide novel evidence that DNAm at loci involved in calcium channel activity and development may contribute to long-term CVD risk beyond known risk factors in type 1 diabetes, particularly in individuals with greater glycemic exposure, warranting further study.

Selecting a significance level in sequential testing procedures for community detection

While there have been numerous sequential algorithms developed to estimate community structure in networks, there is little available guidance and study of what significance level or stopping parameter to use in these sequential testing procedures. Most algorithms rely on prespecifiying the number of communities or use an arbitrary stopping rule. We provide a principled approach to selecting a nominal significance level for sequential community detection procedures by controlling the tolerance ratio, defined as the ratio of underfitting and overfitting probability of estimating the number of clusters in fitting a network. We introduce an algorithm for specifying this significance level from a user-specified tolerance ratio, and demonstrate its utility with a sequential modularity maximization approach in a stochastic block model framework. We evaluate the performance of the proposed algorithm through extensive simulations and demonstrate its utility in controlling the tolerance ratio in single-cell RNA sequencing clustering by cell type and by clustering a congressional voting network.

Power enhancement for testing multi-factor asset pricing models via Fisher’s method

Testing multi-factor asset pricing models is instrumental for asset pricing theory and practice. However, due to the accumulation of errors in estimating high-dimensional parameters, traditional quadratic-form tests such as the Wald test perform poorly against the sparse alternative hypothesis, i.e., a few mispriced assets. Fan et al. (2015b) introduced a powerful testing procedure by adding a power enhancement component to the Wald test statistic and proved power enhancement properties. To provide an alternative to their methodology, we first instantiate the power enhancement component by introducing a new maximum-form test statistic and then study the asymptotic joint distribution of the Wald test statistic and the maximum test statistic. We prove that these two test statistics are asymptotically independent. Given their asymptotic independence, we propose a new power-enhanced testing procedure to combine their respective power based on Fisher’s method (Fisher, 1925). Theoretically, we prove that the new power-enhanced test retains the desired nominal significance level and achieves asymptotically consistent power against more general alternatives. Furthermore, we demonstrate the finite-sample performance of our proposed power-enhanced test in both simulation studies and an empirical study of testing market efficiency using asset returns of the Russel-2000 portfolio.