Hypothesis Of Independence Research Articles

ON THE CONVERGENCE OF RECURSIVE KERNEL DENSITY ESTIMATORS FOR WIDELY ORTHANT DEPENDENT AND CENSORED DATA

In this article, we establish the almost sure convergence of a family of recursive estimators when the data are censored checking widely orthant dependence (WOD). The dependence hypothesis gives this work a certain originality because most often, the study of censored data is done with an independence hypothesis and in reality the real data are often dependent.

Pengaruh Media Pembelajaran Geometri Bangun Ruang Berbasis Google Sites Terhadap Hasil Belajar Kelas V

The purpose of this research is to determine whether there is an influence of Google Sites-based spacial geometry learning media on the learning outcomes of class V students. This type of research is quasi-experimental with a Nonequivalent Control Group Design. The population in this study were all fifth grade students at SDN Bugih 1 Pamekasan. The sampling technique used Simple Random Sampling, with a sample of 44 students. Research data was collected using learning outcomes tests, observations of learning implementation, and student response questionnaires. The research results showed that the average learning outcome for the control class after being treated was 0.00 and the average learning outcome for the experimental class after being treated was 83.33. Then the results of the independent sample t-test hypothesis test from student learning outcomes after being given treatment obtained a significance value of 0.000 and tcount > ttable, namely 4.234 > 2.021. Based on decision making in the independent sample t-test, if the significance value is <0.05 and tcount > ttable, then Ho is rejected and Ha is accepted, that is, there is a significant difference between the learning outcomes of the control class and the experimental class, and the learning outcomes of the class. The experiment was better than the control class learning results. So it can be concluded that there is a significant difference in average learning outcomes between the control class and the experimental class, there is an influence of Google Sites-based spatial geometry learning media on the learning outcomes of class V students at SDN Bugih 1 Pamekasan

Working memory for gaze benefits from the face context.

Retaining gaze in working memory (WM) is essential for successfully navigating through the social world. In the current study, we investigated how WM stores gaze direction by focusing on the role of face context in gaze WM. To address this question, we propose two competing hypotheses. The independence hypothesis predicts that eye gaze is stored independently and is not susceptible to the influence of the surrounding face context. Conversely, the embedding hypothesis claims that gaze WM involves face context and that disruption of holistic face processing would also impair memory for embedded gaze. In three experiments, we adopted different manipulations to disrupt holistic face processing and compared WM performance for gaze within and without face context. In Experiments 1 and 2, we tested WM for gaze direction with schematic upright or inverted faces. We found better performance for gaze within upright faces (vs. inverted faces) by increasing the probability of being remembered. In Experiment 3, we replaced schematic faces with photographic faces, and disrupted holistic processing by using scrambled faces. Results replicated our previous findings, showing that photographic gaze within intact faces was better remembered than gaze presented alone or gaze within scrambled faces. These findings indicate that gaze memory is face-dependent and support the embedding hypothesis.

Influences of Gender, Age and Income Differences on Consumers’ Purchase Items

This paper studies whether gender, age and income differences matter for in-store purchases of selected non-durable and durable consumer goods in the Southwest region of the State of Louisiana. Primary data are collected by face-to-face interviewing of randomly selected adult population within the age groups 15 years to 64 years, and above. Using the collected primary data for categorical variables, -tests are implemented for six separate null hypotheses of statistical independence between/among variables of interest. The results reveal that only gender difference matters the most for in-store purchases of both non-durable and durable goods in the region. Age and income differences to have no significant influences on such purchases. As the findings suggest, the regional in-store retailers should focus more on female shoppers to promote sales of non-durable goods. At the same time, they should focus more on male shoppers for promotion of sales of durable goods in the region. Due cautions are in order for any kind of generalization of the findings of this study, given the extreme regional characteristics of the primary data. Key Words: Retail Sales, In-Store, Non-Durable Goods, Durable Goods, Shoppers, Age, Gender, Income JEL Classifications: D10, D11, D12

Financial Performance Analysis of Bank Aceh's During the Covid-19: RGEC Approaches

The Covid-19 pandemic has had a significant impact on various sectors in Indonesia, including the banking industry. This study aims to compare the financial performance of PT Bank Aceh Syariah before and during the Covid-19 pandemic using the RGEC (Risk Profile, Good Corporate Governance, Earnings, Capital) analysis method. Secondary data used in this research were obtained from the financial statements of Bank Aceh Syariah published on the bank's official website. Data analysis was conducted using the independent sample t-test hypothesis testing. The results indicate that there were no significant differences in the ratios of Non-Performing Financing (NPF), Financing to Deposit Ratio (FDR), Good Corporate Governance (GCG), Return on Assets (ROA), Return on Equity (ROE), Operating Expenses to Operating Income (BOPO), and Capital Adequacy Ratio (CAR) between the periods before and during the Covid-19 pandemic. These findings suggest that Bank Aceh Syariah has been able to maintain its financial performance despite the challenges posed by the pandemic. This study contributes to the understanding of the resilience and adaptability of Islamic banks in the face of economic shocks and highlights the importance of robust risk management and governance practices in ensuring financial stability during times of crisis.

A model assisted probability of detection for guided wave SHM systems: comparative study on recent statistical developments

Despite the potential benefits, industrial implementation of guided wave monitoring systems remains limited today. One of the reason behind this is the lack of consolidated reliability assessment techniques. Recent works clearly illustrated that the classical statistical methodology adopted from nondestructive evaluation to estimate POD is not directly applicable to structural health monitoring (SHM): the hypothesis of independence does not hold for the data obtained from permanently attached sensors and growing defects. The experimental determination of POD for SHM systems requires extensive and costly studies, in addition to the numerous representative structures. Involving numerical studies can complement experimental trials, inform POD evaluation and reduce this cost drastically. We propose in this work a comparative study to demonstrate the application of Model Assisted Probability Of Detection approach (MAPOD) for elastic guided wave monitoring systems. We apply classical and recently developed statistical methods proposed in the literature, using simulated data obtained thanks to the CIVA SHM module. The MAPOD estimation is validated in comparison to experimental studies.

Summary statistics knockoffs inference with family-wise error rate control.

Testing multiple hypotheses of conditional independence with provable error rate control is a fundamental problem with various applications. To infer conditional independence with family-wise error rate (FWER) control when only summary statistics of marginal dependence are accessible, we adopt GhostKnockoff to directly generate knockoff copies of summary statistics and propose a new filter to select features conditionally dependent on the response. In addition, we develop a computationally efficient algorithm to greatly reduce the computational cost of knockoff copies generation without sacrificing power and FWER control. Experiments on simulated data and a real dataset of Alzheimer's disease genetics demonstrate the advantage of the proposed method over existing alternatives in both statistical power and computational efficiency.

Pengaruh Model Pembelajaran Kooperatif Tipe Make A Match Berbantuan Aplikasi 3D terhadap Pemahaman Konsep pada Materi Sistem Pencernaan

This research utilized a cooperative learning model of make a match type assisted by a 3D application to investigate the influence of students' conceptual understanding on the topic of the digestive system in Grade XI of a Private High School in Medan City. The objectives of this study were to determine the impact of using the cooperative learning model of make a match type assisted by a 3D application and to assess students' conceptual understanding of the digestive system material. The research design employed was a non-equivalent control group quasi-experimental approach with a quantitative method. The study was conducted with an experimental group and a control group. The instruments administered to both groups consisted of pretest (before treatment) and posttest (after treatment) essay questions adjusted according to conceptual understanding indicators. Data analysis included descriptive statistical approach, inferential analysis, and independent t-test hypothesis testing to evaluate the influence and differences in students' conceptual understanding between the experimental group (treatment) and the control group using conventional learning models. The results of this study indicated that the posttest scores in the experimental group increased by 73.95 points, compared to only 12.19 points in the control group. The findings suggest a significant impact of the treatment (experimental group) on students' conceptual understanding of the digestive system material in one of the Private High Schools in Medan City.

STRENGTHENING THE STUDENTS’ INDEPENDENT ATTITUDES IN LEARNING

The purpose of this study is to ascertain how the project to raise the Pancasila student profile (p5) has affected the class IV pupils at SDN 10 Sungailiat's autonomous attitude.Quantitative experimental procedures are the research approach that is employed. Questionnaires are the method used in this study to collect data. This study uses a quasi-experimental design with a posttest alone and a non-equivalent control group. The average posttest score for the control class is 51.37, whereas the experimental class's posttest score is 83.96, according to data processing results from the research. The sample is drawn from a population that is regularly distributed, according to the results of the normality test. The results of the homogeneity test demonstrate that each group's variance is the same, or homogeneous. The results of the independent sample t-test hypothesis test show nvalue of significance2-tailednamely 0.000 so Sig. 0,000< 0.05 with a value of tcount 35.217 and ttable 2.00575 so tcount > ttable. Thus Ho is rejected and Ha is accepted, which means there is influenceproject to strengthen the profile of Pancasila students (p5) towards the independent attitude of class IV students at SDN 10 Sungailiat.

Efficient Discovery of Significant Patterns with Few-Shot Resampling

Significant pattern mining is a fundamental task in mining transactional data, requiring to identify patterns significantly associated with the value of a given feature, the target. In several applications, such as biomedicine, basket market analysis, and social networks, the goal is to discover patterns whose association with the target is defined with respect to an underlying population, or process, of which the dataset represents only a collection of observations, or samples. A natural way to capture the association of a pattern with the target is to consider its statistical significance , assessing its deviation from the (null) hypothesis of independence between the pattern and the target. While several algorithms have been proposed to find statistically significant patterns, it remains a computationally demanding task, and for complex patterns such as subgroups, no efficient solution exists. We present FSR, an efficient algorithm to identify statistically significant patterns with rigorous guarantees on the probability of false discoveries. FSR builds on a novel general framework for mining significant patterns that captures some of the most commonly considered patterns, including itemsets, sequential patterns, and subgroups. FSR uses a small number of resampled datasets, obtained by assigning i.i.d. labels to each transaction, to rigorously bound the supremum deviation of a quality statistic measuring the significance of patterns. FSR builds on novel tight bounds on the supremum deviation that require to mine a small number of resampled datasets, while providing a high effectiveness in discovering significant patterns. As a test case, we consider significant subgroup mining, and our evaluation on several real datasets shows that FSR is effective in discovering significant subgroups, while requiring a small number of resampled datasets.

Venous valve hypoxia as a possible mechanism of deep vein thrombosis: a scoping review.

The pathogenesis of deep vein thrombosis (DVT) has been explained by an interplay between a changed blood composition, vein wall alteration, and blood flow abnormalities. A comprehensive investigation of these components of DVT pathogenesis has substantially promoted our understanding of thrombogenesis in the venous system. Meanwhile, the process of DVT initiation remains obscure. This systematic review aims to collect, analyze, and synthesize the published evidence to propose hypoxia as a possible trigger of DVT. An exhaustive literature search was conducted across multiple electronic databased including PubMed, EMBASE, Scopus, and Web of Science to identify studies pertinent to the research hypothesis. The search was aimed at exploring the connection between hypoxia, reoxygenation, and the initiation of deep vein thrombosis (DVT). The following key words were used: "deep vein thrombosis," "venous thrombosis," "venous thromboembolism," "hypoxia," "reoxygenation," "venous valve," and "venous endothelium." Reviews, case reports, editorials, and letters were excluded. Based on the systematic search outcome, 156 original papers relevant to the issue were selected for detailed review. These studies encompassed a range of experimental and observational clinical research, focusing on various aspects of DVT, including the anatomical, physiological, and cellular bases of the disease. A number of studies suggested limitations in the traditional understanding of Virchow's triad as an acceptable explanation for DVT initiation. Emerging evidence points to more complex interactions and additional factors that may be critical in the early stages of thrombogenesis. The role of venous valves has been recognized but remains underappreciated, with several studies indicating that these sites may act as primary loci for thrombus formation. A collection of studies describes the effects of hypoxia on venous endothelial cells at the cellular and molecular levels. Hypoxia influences several pathways that regulate endothelial cell permeability, inflammatory response, and procoagulation activity, underpinning the endothelial dysfunction noted in DVT. Hypoxia of the venous valve may serve as an independent hypothesis to outline the DVT triggering process. Further research projects in this field may discover new molecular pathways responsible for the disease and suggest new therapeutic targets.

Robust point cloud registration for map-based autonomous robot navigation

Robust point cloud registration for map-based autonomous robot navigation

تأثير استخدام وسيلة الكلمات المتقاطعة في تدريس المفردات: دراسة تجريبية في المدرسة لقمان الإيتدائية الإسلامية المتكاملة بادنج

The aim of this research is to reveal the influence of crossword puzzle media on the Arabic vocabulary skills of students in SDIT Luqman Padang. As for its research approach, it is a quantitative approach using experimental research methods based on field research. Data collection tools are tests, observations, interviews, and documents. The method of data analysis that researchers use is quantitative data analysis with the help of the SPSS 26 program. The results of the study were: 1) The ability of students to learn vocabulary appears from the average pre-test scores in the control class of 71.85 with the category "acceptable" and in the experimental class of 73.65 with the category "acceptable." 2) The use of crossword puzzle media to teach vocabulary consists of three stages: preliminary, primary, and final activities. 3) The impact of using crossword puzzle media in the teaching of vocabulary is acceptable. This statement emerged from the test results of an independent hypothesis sample t-test with a value of 0.000 less than 0.05, meaning Ha was accepted and Ho was rejected. Therefore, the researchers concluded that there was an influence on the use of crossword puzzle media in improving the Arabic vocabulary skills of fourth-grade students at the SDIT Luqman Padang.

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.

Investigation of the influence of the elastic medium on the dynamic behavior of a three-layer elliptical cylinder under non-stationary loading

Dynamic transient processes are studied and the results of evaluating the influence of an elastic medium on the behavior of a three-layer cylindrical shell structure of elliptical cross-section under non-stationary impulse loading are given. The used model of the theory of shells and rods by S. P. Tymoshenko, taking into account independent static and kinematic hypotheses for each layer of the structure. Numerical calculations of normal deflections and normal stresses of the load-bearing layers of the structure, which determine its stress-strain state (SSS), have been performed. Variants of the structure without a polymer aggregate and with a discrete-symmetric rib-reinforced aggregate are considered. A comparative analysis of the deflections and stresses of the load-bearing layers of the structure in the absence of light aggregate and in its presence is given.Numerical results regarding the dynamics of the three-layer structure were obtained using the finite element method.

The unwritten practice of academic benchmarking in organizational hierarchy—An analysis

The paper investigates the reasons why decision makers often prefer individuals with high academic credentials for promotion and leadership positions. It also examines how the process of attaining higher education develops special personality traits, which hold the attention of decision makers favorably, and seem to be crucial for the organizational success. The study further explores the correlation between higher educational attainments and corporate hierarchy.The findings of our survey are discussed in the light of both qualitative and quantitative analyses of the data. The result of the Pearson's Chi-square test rejects null hypothesis of independence between higher academic credentials and corporate hierarchy. The p-values at different levels of significance confirm that the results are significant and highly unlikely to have occurred by chance alone.Based on Tau coefficient of the Kendall's test statistic, the magnitude of correlation between the said variables is found to be +0.33 with positive direction of correlation. This suggests that higher academic credentials can contribute about 33% to the total appraisal score given by an appraisal committee to a prospective candidate for direct placement or advancement to a leadership position in corporate hierarchy.

A normal test for independence via generalized mutual information

Testing hypothesis of independence between two random elements on a joint alphabet is an important exercise in statistics. Pearson’s chi-squared test is effective for dense contingency tables. General statistical tools are lacking when the contingency tables are non-ordinal and sparse. This article proposes a test based on generalized mutual information, with two main advantages: (1) the test statistic is asymptotically normal under the independence hypothesis (provided the marginals are not uniformly distributed), consequently it does not require the knowledge of the row and column sizes of the contingency table, and (2) the test is consistent and therefore it detects any dependence structure in the general alternative space given a sufficiently large sample. Simulation studies show that the proposed test converges faster than Pearson’s chi-squared test when the contingency table is sparse.

Kernel-based method for joint independence of functional variables

This work investigates the problem of testing whether d functional random variables are jointly independent, using a modified estimator of the d-variable Hilbert Schmidt Indepedence Criterion (dHSIC). We then get the asymptotic normality of this estimator both under the joint independence hypothesis and under the alternative hypothesis. A simulation study shows the good performance of the proposed test on a finite sample.

Multiple sources of unconscious-information processing affect a single response: independent unconscious priming effects

At present there is little knowledge on whether and how multiple pieces of unconscious information can simultaneously affect a single conscious response. In the present study, we manipulated the congruency relation between a masked prime arrow and the target arrow, as well as that between masked flankers and the target arrow. The results demonstrated that the masked prime and flankers produced independent unconscious priming effects on the response to the target. In the process of studying the above phenomenon, two secondary findings were made. First, although the prime congruency effect was obtained, the flanker congruency effect was smaller when the flankers were displayed simultaneously with the target than when they were displayed sequentially before the target. This suggested that priming stimulation required enough time to be processed to a sufficient extent to produce an unconscious priming effect. Second, when the prime stimulus was removed, leaving only the flankers, the flanker priming effect increased, suggesting that the attention attracted to the prime and its conscious mask could also reduce the flanker congruency effect. These results observed across several experiments were replicated in one within-subjects experiment. We proposed an “independent unconscious influence hypothesis” for the phenomenon. This hypothesis was further integrated into a more comprehensive unconscious information processing model. The possible causes of the observed phenomena were discussed.

Deep Multi-Instance Conv-Transformer Frameworks for Landmark-Based Brain MRI Classification

For brain diseases, e.g., autism spectrum disorder (ASD), with unclear biological characteristics, the detection of imaging-based biomarkers is a critical task for diagnosis. Several landmark-based categorization approaches have been developed for the computer-aided diagnosis of brain diseases, such as Alzheimer’s disease (AD), utilizing structural magnetic resonance imaging (sMRI). With the automatic detection of the landmarks of brain disease, more detailed brain features were identified for clinical diagnosis. Multi-instance learning is an effective technique for classifying brain diseases based on landmarks. The multiple-instance learning approach relies on the assumption of independent distribution hypotheses and is mostly focused on local information, thus the correlation among different brain regions may be ignored. However, according to previous research on ASD and AD, the abnormal development of different brain regions is highly correlated. Vision Transformers, with self-attention modules to capture the relationship between embedded patches from a whole image, have recently demonstrated superior performances in many computer vision tasks. Nevertheless, the utilization of 3D brain MRIs imposes a substantial computational load, especially while training with Vision Transformer. To address the challenges mentioned above, in this research, we proposed a landmark-based multi-instance Conv-Transformer (LD-MILCT) framework as a solution to the aforementioned issues in brain disease diagnosis. In this network, a two-stage multi-instance learning strategy was proposed to explore both spatial and morphological information between different brain regions; the Vision Transformer utilizes a multi-instance learning head (MIL head) to fully utilize the features that are not involved in the ultimate classification. We assessed our proposed framework using T1-weighted MRI images from both AD and ASD databases. Our method outperformed existing deep learning and landmark-based methods in terms of brain MRI classification tasks.