Independent Tests Of Hypothesis Research Articles

An Efficient Spoofing Attack Detection Using Deep Learning Based Physical Layer Security Technique

Spoofing attack detection plays a crucial role in the defence field, involving critical and highly secured data processing. The accurate attack detection mechanism prevents unauthorised access to sensitive information, thereby protecting National security. Physical Layer Security (PLS) is a promising emerging technique that uses the wireless channel’s randomness to secure the communication network. The spoofing attack is one of the severe threats to the wireless network, where the attacker imitates the legitimate user to launch an attack against the network. This paper investigates the channel characteristics-based physical layer technique to detect spoofing attacks. For static radio environments, the two-sample independent hypothesis testing is used to identify the spoofing attack, showing an improvement in detection accuracy of 97 %. The attack detection problem is considered a Reinforcement Learning (RL) based classification problem for a challenging dynamic radio environment. It is simulated using the actor-critic-based Deep Reinforcement Learning (DRL) technique with the help of the Reformed Deep Deterministic Policy Gradient (Re-DDPG) algorithm. The simulated results show that the proposed method performs better than the existing strategies and achieves a Receiver Operating Characteristics (ROC) value of 0.96. The detection accuracy of the proposed method can reach up to 98 %, with precision and recall of about 98 % and 99 %, respectively.

Trial-by-trial mouse trajectory predicts variance in precision across working memory representations: A critical reanalysis of Hao et al. (2021).

Multiple representations in visual working memory (VWM) can vary in mnemonic precision. This inhomogeneity of VWM precision has received some support from recent studies with the whole-report procedure, in which all memory items are recalled in free or forced orders. Recently, Hao et al. (2021, Cognition, 214, 104739) added a novel item-selection stage before each memory recall and found smaller between-trial variance in mouse trajectory during the selection stage in free-recall condition as compared with forced recall, which was taken as evidence for less between-item interference and the resulting precision benefit under free recall. Here, we reanalyzed the original dataset with a different analytic approach and attempted independent hypothesis testing focusing on within-trial trajectory deviations. We found that the direction of trial-by-trial trajectory bias for the first to-be-recalled item was predictive of the relative mnemonic precision of the remaining items. Critically, this relationship was only present for forced recall but not for free recall. Hierarchical Bayesian modeling of recall errors further identified that this relationship was selectively driven by VWM precision. Together, our reanalysis provides evidence for the source of between-item interference and its direct association with variable precision of VWM representations, and further highlights the novel methodological benefits of probing memory decisional processes using mouse trajectory data.

Detecting nonlinear stochastic systems using two independent hypothesis tests.

Various systems in the real world can be nonlinear and stochastic, but because nonlinear time series analysis has been developed to distinguish nonlinear deterministic systems from linear stochastic systems, there have been no appropriate methods developed so far for testing the nonlinear stochasticity for a given system. Thus, here we propose a set of two hypothesis tests, one for the nonlinearity and one for the stochasticity, independent of each other. The test for the linearity is based on Fourier-transform-based surrogate data with a nonlinear test statistic, while the test for determinism depends on the theory of ordinal patterns or permutations recently developed intensively. We demonstrate the proposed set of tests with time series generated from toy models. In addition, we show that both a foreign exchange market and a temperature series in Tokyo could be nonlinear and stochastic, as well as sometimes with determinism beyond pseudoperiodicity.

Analysis of the Effect of Multiple Testing in Assessing Tobacco Product Differences

Summary During the last two decades, an increase of tobacco product reporting requirements from regulators was observed, such as Europe, Canada or USA. However, the capacity to compare and discriminate accurately two products is impacted by the number of constituents used for the comparison. Indeed, performing a large number of simultaneous independent hypothesis tests increases the probability of rejection of the null hypothesis when it should not be rejected. This leads to virtually guarantee the presence of type I errors among the findings. Correction methods have been developed to overcome this issue like the Bonferroni or Benjamini & Hochberg ones. The performance of these methods was assessed by comparing identical tobacco products with different sizes of data sets. Results showed that multiple comparisons lead to erroneous conclusions if the risk of type I error is not corrected. Unfortunately, reducing the type I error impacts the statistical power of the tests. Consequently, strategies for dealing with multiplicity of data should provide a reasonable balance between testing requirement and statistical power of differentiation. Multiple testing for product comparison is less of a problem if studies restrict to the most relevant parameters for comparison.

Assortative versus disassortative mating preferences of female zebra finches based on self-referent phenotype matching

Kin recognition abilities allow individuals to treat relatives differently. In mate choice contexts, kin recognition can ensure individuals avoid the costs of inbreeding. However, there is also the potential for assortative preferences for genetic similarity as part of an optimal outbreeding strategy. We tested the kin recognition abilities of captive female zebra finches, Taeniopygia guttata, in standard mate choice trials. A full individual cross-fostering scheme ensured that all subjects grew up in broods of only unrelated nestmates and were raised by unrelated foster parents. Therefore, individuals could use only self-referent phenotype matching (or recognition alleles), since exposure to genetic kin was excluded completely. Females were allowed the simultaneous choice between an unfamiliar genetic brother and unfamiliar, unrelated stimulus males. We used three cohorts of birds (199 females in total) that allowed three independent hypothesis tests. We found a significant avoidance of the unfamiliar brother in the first cohort, but could not replicate this finding in the other two cohorts. This was not easily explained by differences in the treatment of the cohorts, since the difference between cohorts was nonsignificant and testable differences in treatments did not show significant effects. The combined effect size was very low (d = −0.048) and nonsignificant. The initial finding of disassortative preferences may represent a type I error or there may be a true effect that is very weak and not easily reproducible. In any case, we did not find evidence of assortative preferences based on self-referent phenotype matching.

Parametric and Nonparametric FDR Estimation Revisited

Nonparametric and parametric approaches have been proposed to estimate false discovery rate under the independent hypothesis testing assumption. The parametric approach has been shown to have better performance than the nonparametric approaches. In this article, we study the nonparametric approaches and quantify the underlying relations between parametric and nonparametric approaches. Our study reveals the conservative nature of the nonparametric approaches, and establishes the connections between the empirical Bayes method and p-value-based nonparametric methods. Based on our results, we advocate using the parametric approach, or directly modeling the test statistics using the empirical Bayes method.

Microbial biodiversity: approaches to experimental design and hypothesis testing in primary scientific literature from 1975 to 1999.

Research interest in microbial biodiversity over the past 25 years has increased markedly as microbiologists have become interested in the significance of biodiversity for ecological processes and as the industrial, medical, and agricultural applications of this diversity have evolved. One major challenge for studies of microbial habitats is how to account for the diversity of extremely large and heterogeneous populations with samples that represent only a very small fraction of these populations. This review presents an analysis of the way in which the field of microbial biodiversity has exploited sampling, experimental design, and the process of hypothesis testing to meet this challenge. This review is based on a systematic analysis of 753 publications randomly sampled from the primary scientific literature from 1975 to 1999 concerning the microbial biodiversity of eight habitats related to water, soil, plants, and food. These publications illustrate a dominant and growing interest in questions concerning the effect of specific environmental factors on microbial biodiversity, the spatial and temporal heterogeneity of this biodiversity, and quantitative measures of population structure for most of the habitats covered here. Nevertheless, our analysis reveals that descriptions of sampling strategies or other information concerning the representativeness of the sample are often missing from publications, that there is very limited use of statistical tests of hypotheses, and that only a very few publications report the results of multiple independent tests of hypotheses. Examples are cited of different approaches and constraints to experimental design and hypothesis testing in studies of microbial biodiversity. To prompt a more rigorous approach to unambiguous evaluation of the impact of microbial biodiversity on ecological processes, we present guidelines for reporting information about experimental design, sampling strategies, and analyses of results in publications concerning microbial biodiversity.

Exact Bahadur slope for combining independent tests for normal and logistic distributions

Combining independent tests of hypotheses is an important and popular statistical practice. Usually, data about a certain phenomena come from different sources in different times, so we want to combine these data to study these phenomena. Several combination methods were used to combine infinity independent tests. These methods are Fisher, logistic, sum of P-values and inverse normal for testing simple hypotheses against one-sided alternative. These methods are compared via the exact Bahadur slope (EBS). These non-parametric methods depend on the P-value of the individual tests combined.

Meta-analysis of cognitive-behavioral treatment studies for bulimia

A meta-analysis was performed to systematically assess the effect of cognitive-behavioral treatments for bulimia. To protect against past criticisms of meta-analyses, this study focused on well-defined hypotheses with clearly articulated conceptual foundations. Twenty-six studies of the cognitive-behavioral treatment of bulimia were selected through computer searches. Effect sizes were calculated for changes in behavioral outcome measures (25 independent hypothesis tests) and cognitive-attitudinal outcome measures (17 independent hypothesis tests). Additionally, two effect sizes were generated for within and between group comparisons. The analysis revealed an effect size of average r = 0.69 for behavioral outcome measures (average r = 0.64 for between group and average r = 0.74 for within group) and average r = 0.67 for cognitive-attitudinal outcome measures (average r = 0.64 for between group and average r = 0.69 for within group). Follow-up effect sizes were less favorable; however, the diversity of time spans and outcome measures used to calculate follow-up effect sizes limit their utility. Overall, results suggest that the use of a cognitive-behavioral therapy will result in favorable treatment outcomes and implications for future research are discussed.

Testing exact aggregation in income and household characteristics: the effects of aggregation across goods

Exact aggregation in income and household characteristics are tested using Canadian cross-sectional microdata. Over 100 data sets of homogeneous households are used, making it possible to conduct a large number of independent hypothesis tests. Tests are also conducted to determine whether the homogeneous household groups can be grouped into more heterogeneous data sets. Six different kinds of demand systems are estimated to ensure the robustness of the results to separability/aggregation structure. Exact aggregation in income, family size, region of residence and housing tenure status are strongly rejected, but exact aggregation in age of head of household is not. Restrictions which would allow pooling of households of different sizes, regions and tenure status are also strongly rejected. The test results exhibit a little sensitivity to the expenditure aggregates used, but not to the extent that the results would be different. The results imply that aggregate demand functions which use time series data should include many statistics from the joint distribution of income and household characteristics. In addition, when using data at the micro-level, the amount of heterogeneity in households' behaviour is much greater than usually assumed. This calls for the use of much more homogeneous household groups in the estimation of demand models than are typically employed.

Crystal Axes in Recent and Fossil Adult Echinoids Indicate Trophic Mode in Larval Development

The type of larval development in echinoids (Phylum Echinodermata) was found to determine crystal patterns in adult apical skeletal plates. This relation between larvae and adults will allow fossil species to be examined to determine the effects of developmental mode on evolutionary patterns, such as species longevity and speciation rate. Independent tests of hypotheses of such patterns now shown among fossil mollusks with and without feeding larvae can thus be performed.

The weighted compounding of probabilities from three independent hypothesis tests

Tail probabilities from three independent hypothesis tests can be combined to form a test statistic of the form P1,P2 θ2,P3 θ3.The null distribution of the combined test statistic is presented and critical values for α=0.01 and 0.05 are provided.The power of this test is discussed for the special case ofthree independent F-tests.

Meta-Analysis and Cross-Cultural Comparison

This article argues for the application of meta-analysis to cross-cultural comparison as an adjunct to traditional review methods. The objective means of combining results of independent hypothesis tests provided by meta-analysis is illustrated through an examination of the literature on sex differences in child competitiveness. The results indicate an overall tendency for males to be more competitive than females. However, analysis within cultures indicates that this is not a universal trend. The utility of meta-analysis and the problems encountered in its cross-cultural application are discussed. It is concluded that the use of meta-analysis in conjunction with cross-cultural comparison provides a means of theory advancement of great potential.

Asymptotic Optimality of Fisher's Method of Combining Independent Tests

Abstract Four methods of combining independent tests of hypothesis are compared via exact Bahadur relative efficiency. The methods considered are Fisher's method, the mean of the normal transforms of the significance levels, the maximum significance level, and the minimum significance level. None of these is uniformly more powerful than the others, but, according to Bahadur efficiency, Fisher's method is the most efficient of the four. In some cases, Fisher's method is most efficient of all tests based on the data, but this is not generally true.

Asymptotic Optimality of Fisher's Method of Combining Independent Tests

Abstract Four methods of combining independent tests of hypothesis are compared via exact Bahadur relative efficiency. The methods considered are Fisher's method, the mean of the normal transforms of the significance levels, the maximum significance level, and the minimum significance level. None of these is uniformly more powerful than the others, but, according to Bahadur efficiency, Fisher's method is the most efficient of the four. In some cases, Fisher's method is most efficient of all tests based on the data, but this is not generally true.