Statistical Testing Approach Research Articles

The Effect of Regional Fiscal Capacity on Climates Change Action Budgeting Commitments

This journal aims to determine the effect of regional fiscal capacity on climate change action budgeting commitments. The research method used is a quantitative method with a statistical test approach to Structural Equation Modeling (SEM). The results of this study indicate that the local government's fiscal capacity of Gorontalo Regency has a significant positive direct influence on climate change action budgeting commitments.

Using Bayesian hierarchical analysis to combine evidence across outcomes and trials

AbstractBackgroundThe FDA’s statistical review for the recent aducanumab advisory committee included a Bayesian hierarchical analysis (BHA) combining CDR‐SB evidence across the ENGAGE and EMERGE studies resulting in a 62% probability of the treatment being efficacious. BHA can combine evidence across outcomes, trials within a drug development program, and trials for a specific mechanism of action, and provides an elegant solution to interpreting the totality of evidence. BHA is a comparable approach to composite scores and global statistical tests, but key differences exist.MethodUsing summary results from the aducanumab ENGAGE and EMERGE studies, solanezumab Expedition 1, 2, and 3 and the BAN2401 PRIME study, we combined evidence from ADAS‐Cog, ADCS‐ADL, and CDR‐SB using a BHA with hierarchies at the study, program, and three program level to assess the amyloid hypothesis, using the pre‐specified primary analysis populations. We compared these results to GSTs.ResultsThe aducanumab program combined across EMERGE and ENGAGE with all 3 outcomes has a 0.92 probability (>99.9% with GST), the solanezumab program combined across Expedition 1, Expedition 2 and Expedition 3 has a 0.92 probability (>99.9% with GST), and the BAN2401 program has a 0.95 probability (99.2% with GST) of treatment efficacy. We present BHA and GST results for individual studies compared to the original results. We also compare the study‐level BHA results to the global statistical test (GST) approach. We combine evidence across all 3 programs to get an overall probability of efficacy for the amyloid hypothesis with these 3 programs using BHA and GST.ConclusionBHA and GST both assess the totality of evidence within a study or development program, providing a natural correction for multiplicity, but results differ. BHA penalizes for divergence of outcomes or studies, and GST does not. This may be appropriate in some settings, but not in others. It allows an assessment of convergence of evidence, weakening of evidence, or elimination of evidence when a negative study “cancels out” a positive study. On a larger scale, it sheds light on how much evidence might exist for theories like the amyloid hypothesis when combining evidence across programs.

Design of functional observers for fault detection and isolation in nonlinear systems in the presence of noises

This work deals with the problem of designing functional observers for fault diagnosis in nonlinear systems in the presence of noises. It follows up previous work of the authors on design of functional observers (residual generators) for deterministic systems from the point of view of observer error linearization. Here, we consider the effect of noises on residual generation. The effect of sensor noises on the residuals is studied analytically and the associated probability distributions are derived. Following this, a well-known statistical hypothesis testing approach, Generalized Likelihood Ratio, is used to track changes in the mean of the residual to enable robust fault detection. The approach is also extended to process noises (plant-model mismatch) numerically. Throughout the study the methods presented are tested on a non-isothermal CSTR case study. The results show that the fault diagnosis scheme is able to quickly and accurately detect faults in the presence of both sensor and process noises.

The Influence of Corporate Ethics Culture on Whistleblowing Mechanisms and Their Impact on Fraud Detection (Empirical Study in the Anti Fraud Division of PT. Bank XYZ in Jakarta 2020)

Fraud is one of the main problems for many organizations today. The more cases of fraud that occur in the banking sector in Indonesia, it is necessary to have fraud prevention efforts. This study aims to examine the effect of corporate ethical culture on the whistleblowing mechanism and its impact on fraud detection. This research was conducted using an explanatory quantitative research method with a survey through distributing questionnaires to all anti-fraud employees and the internal audit work unit who were directly involved in the whistleblowing mechanism at Bank XYZ who became respondents. The total respondents in this study amounted to 89 respondents. Statistical testing in this study used a statistical test approach with Smart PLS software version 3.0. The results of this study indicate that the company's ethical culture has a significant positive effect on the whistleblowing mechanism, the whistleblowing mechanism has a significant positive effect on fraud detection and the company's ethical culture has a significant positive effect on fraud detection with the whistleblowing mechanism while the influence of corporate ethical culture has no significant effect on fraud detection.

Fundamental scaling laws of covert DDoS attacks

Botnets such as Mirai use insecure home devices to conduct distributed denial of service attacks on the Internet infrastructure. Although some of those attacks involve large amounts of traffic, they are generated from a large number of homes, which hampers their early detection. In this paper, our goal is to answer the following question: what is the maximum amount of damage that a DDoS attacker can produce at the network edge without being detected? To that aim, we consider a statistical hypothesis testing approach for attack detection at the network edge. The proposed system assesses the goodness of fit of traffic models based on the ratio of their likelihoods. Under such a model, we show that the amount of traffic that can be generated by a covert attacker scales according to the square root of the number of compromised homes. We evaluate and validate the theoretical results using real data collected from thousands of home-routers connected to a mid-sized ISP.

On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives

Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. So far, the MRCDI data acquisition achieves only a low signal-to-noise ratio, does not provide a full volume coverage and lacks data from the scalp and skull regions. In addition, it is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. The reconstruction problem thus involves coping with noisy and incomplete data, which makes it mathematically challenging. Most existing reconstruction methods have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data.We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

Efficacy and safety of sotagliflozin in patients with type 2 diabetes and severe renal impairment.

To assess the efficacy and safety of sotagliflozin, a dual inhibitor of sodium-glucose cotransporter-1 and -2, in adults with type 2 diabetes (T2D) and stage 4 chronic kidney disease (CKD4). This 52-week, phase 3, randomized (1:1:1), placebo-controlled trial evaluated sotagliflozin 200 mg and sotagliflozin 400 mg once daily in 277 patients with T2D and estimated glomerular filtration rate (eGFR) 15 to 30 mL/min/1.73 m2 . The primary endpoint was glycated haemoglobin (HbA1c) reduction with sotagliflozin 400 mg versus placebo at 26 weeks. A hierarchical statistical testing approach was used. The baseline mean HbA1c was 65 ± 12 mmol/mol (8.1% ± 1.1%), systolic blood pressure (SBP) was 144 ± 15 mmHg, and eGFR was 24 ± 4mL/min/1.73m2 . Placebo-adjusted changes with sotagliflozin 400 mg were -3 mmol/mol (-0.3%; 95% confidence interval -7 to 0.6 [-0.6 to 0.05]; P=0.096) and -8 mmol/mol (-0.7%; -13 to -3 [-1.2 to -0.2]; P=0.003) in HbA1c at Weeks 26 and 52, respectively, -1.5kg (-3.0 to -0.1) in body weight at Week 26, -5.4 mmHg (-9.4 to -1.3) in SBP at Week 12, and -0.3mL/min/1.73 m2 (-2.1 to 1.6; P=0.776) in eGFR at Week 52. Over 52 weeks, 11.8%, 5.4% and 3.3% of patients receiving placebo and sotagliflozin 200 and 400 mg, respectively, required rescue therapy for hyperglycaemia. Adverse events (AEs) occurred in 82.8%, 86.2% and 81.1% of patients and serious cardiovascular AEs occurred in 12.9%, 3.2% and 4.4% of patients in the placebo and sotagliflozin 200 and 400 mg groups, respectively. After 26 weeks, HbA1c reductions with sotagliflozin were not statistically significant versus placebo in adults with T2D and CKD4. The 52-week safety profile was consistent with results of the SCORED outcomes trial (NCT03242018).

Community Perspective on Environmental Pollution in the Citarum River, Karawang-West Java

The use of polluted Citarum River water can cause various diseases, including diarrhea, skin diseases, respiratory infections, etc. This study examines the causes of Citarum River pollution in terms of how residents care about environmental conservation aspects of the Citarum River. The researchers used qualitative methods using questionnaires to explore public perceptions. This method explores four perceptions covering four main aspects: public perception, community participation, socio-economic community, and sanitation waste aspects. Questionnaire analysis with a bivariate statistical test approach and Cronbach's alpha test has the advantage of being able to clearly describe the updated condition of the Citarum River because the data can be justified. After all, it has a high level of confidence of around 95 percent. The results showed that in terms of public perception of the Citarum River, 68% were good, while in terms of community participation in preserving the Citarum River, 26% of the people also participated well, and 32% participated moderately in the maintenance of the Citarum River. As many as 42% of the community consisting of 27% less participate and 15% do not participate. The results of the questionnaire Socio-economy of the Citarum River community are classified as having a fairly good economic condition 59% and a moderate category 41%. The waste and sanitation questionnaire results showed that public awareness about waste and sanitation was also high. 45% percent of the community has a good awareness of the environment around the Citarum River, and 38% have sufficient awareness.

Urban attractors: Discovering patterns in regions of attraction in cities.

Understanding the dynamics by which urban areas attract visitors is important in today’s cities that are continuously increasing in population towards higher densities. Identifying services that relate to highly attractive districts is useful to make policies regarding the placement of such places. Thus, we present a framework for classifying districts in cities by their attractiveness to daily commuters and relating Points of Interests (POIs) types to districts’ attraction patterns. We used Origin-Destination matrices (ODs) mined from cell phone data that capture the flow of trips between each pair of places in Riyadh, Saudi Arabia. We define the attraction profile for a place based on three main statistical features: The number of visitors a place received, the distribution of distance traveled by visitors on the road network, and the spatial spread of locations from where trips started. We used a hierarchical clustering algorithm to classify all places in the city by their features of attraction. We discovered three main types of Urban Attractors in Riyadh during the morning period: Global, which are significant places in the city, Downtown, which contains the central business district, and Residential attractors. In addition, we uncovered what makes districts possess certain attraction patterns. We used a statistical significance testing approach to quantify the relationship between Points of Interests (POIs) types (services) and the patterns of Urban Attractors detected.

The Effectiveness of Oxytocin and Marmet Massage on Increased Prolactin Hormone for Smooth Breastfeeding in Postpartum Mothers in Langsa City Health Office, Indonesia

BACKGROUND: One of the causes of the disruption to the achievement of exclusive breastfeeding is the decrease in milk production experienced by nursing mothers. It is believed that the prolactin hormone and the mother’s perception of breast milk are inadequate. One effort to increase the prolactin hormone is by massaging techniques. This technique has been used by various people in the world for both medical treatment and health care.
 AIM: The purpose of this study was to analyze the differences in the effectiveness of Oxytocin and Marble Massage on Prolactin Hormone levels for smooth breast milk in postpartum women in the working areas of Langsa City Health Centre.
 METHODS: A quasi-experimental design method prospective cohort was used with no control group design. Giving oxytocin massage treatment and Mamet massage for prolactin hormone levels for smooth milk. The sample was selected of 32 postpartum mothers who were divided into two groups. The number of samples for each group was 16 postpartum mothers per group. Data analysis used the T-independent statistical test approach for bivariate analysis and multivariate analysis.
 RESULTS: The results showed that the highest prolactin hormone level in the Massage method was 1178.0 ƞg/ml and the highest prolactin hormone level was 357.9 ƞg/ml, the highest was 357.9 ƞg/ml. The mean prolactin hormone level in the oxytocin massage group was 195.694 ƞg/ml and in the Mamet group was 538.195 ƞg/ml. The average level of the prolactin hormone by the oxytocin massage method was 195.6 ƞg/ml with Std. deviation 106.8 ƞg/ml while the average method for Mamet massage is 538.9 ƞg/ml with Std. deviation 269.6 ƞg/ml. The results of the statistical test Independent T-test obtained Prolactin Hormone Levels for the Smooth ASI value of p = 0.000, then it can be concluded that there are significant differences between the oxytocin and Mamet massage methods.
 CONCLUSION: There is a significant difference between the oxytocin massage method and Mamet to increase levels of the hormone prolactin in postpartum mothers.

Statistical Modeling and Analysis of k-Layer Coverage of Two-Dimensional Materials in Inkjet Printing Processes

ABSTRACT Two-dimensional layered materials/flakes, also known as crystalline atom-thick layer nanosheets, have recently been receiving great attention in electronics fabrication due to their unique and intriguing properties. The k-layer coverage area (i.e., the area covered by k number of overlapping layers) of the printed flake pattern significantly impacts on the properties of the printed electronics. In this work, we constructed a statistical model to describe the k-layer coverage of randomly distributed two-dimensional materials. A series of results are obtained to provide not only the expectation but also the variance of the coverage area. The boundary effects on the random flakes coverage are also studied. In addition, an approximated statistical testing approach is also developed in this work to detect abnormal coverage patterns. The case studies based on simulated data and real flakes images obtained from the inkjet printing process demonstrate the accuracy and effectiveness of the proposed model and analysis methods.

Developing an Automated Technique to Calibrate the AASHTOWare Pavement ME Design Software

Many state highway agencies are in the process of implementing the AASHTOWare Pavement ME Design (PMED) software for routine pavement design. However, a recurring implementation challenge has been the need to locally calibrate the software to reflect an agency’s design and construction practices, materials, and climate. This study introduced a framework to automate the calibration processes of the PMED performance models. This automated technique can search PMED output files and identify relevant damages/distresses for a project on a particular date. After obtaining this damage/distress information, the technique conducts model verification with the global calibration factors. Transfer function coefficients are then automatically derived following an optimization technique and numerical measures of goodness-of-fit. An equivalence statistical testing approach is conducted to ensure predicted performance results are in agreement with the measured data. The automated technique allows users to select one of three sampling approaches: split sampling, jackknifing, or bootstrapping. Based on the sampling approach chosen, the automated technique provides the calibration coefficients or suitable ranges for the coefficients and shows the results graphically. Model bias, standard error, sum squared error, and p-value from the paired t-test are also reported to assess efficacy of the calibration process.

Possibility Measure of Accepting Statistical Hypothesis

Taking advantage of the possibility of fuzzy test statistic falling in the rejection region, a statistical hypothesis testing approach for fuzzy data is proposed in this study. In contrast to classical statistical testing, which yields a binary decision to reject or to accept a null hypothesis, the proposed approach is to determine the possibility of accepting a null hypothesis (or alternative hypothesis). When data are crisp, the proposed approach reduces to the classical hypothesis testing approach.

Group sequential designs applied in psychological research

Psychological research is confronted with ever-increasing demands to save resources such as time and money while assuring high ethical standards. In medical and pharmaceutical research, group sequential designs have fundamentally changed traditional statistical testing approaches featuring only one analysis at the end of a single-stage study. They enable early stopping at an interim stage, after a group of observations, for efficacy or futility in case of an overwhelmingly large or small effect, respectively. Otherwise, the trial is continued to the next stage. On average over many studies time and money are saved and more ethical trials are facilitated by diminishing the risk of patients' exposure to inferior treatments. We provide an easy-to-use tutorial for psychological research replete with easily understandable figures highlighting the core idea of different group sequential designs, a workflow chart, an empirical real-world data set, and the annotated R code. Finally, we demonstrate the application of early stopping for efficacy.

Identifying the presence of structural damage: A statistical hypothesis testing approach combined with residual strain energy

A statistical hypothesis testing scheme is presented for damage detection considering environmental variations, in which the presence of damage is identified via Welch’s t-test by comparing the means of two groups of residual strain energy (RSE) samples from healthy and inspected structures. The RSE, as a damage indication feature, is determined from the mode shape residual (MSR), and the corresponding effects of environmental variations are eliminated via the PCA method. To further improve the damage sensitivity of RSE samples, an MSR selection strategy is proposed to examine the sensitivity of the available MSR matrices to damage and select the most effective MSR matrix. The sensitivity formula of the test statistic with respect to damage is derived and defined as the sensitivity index. Moreover, the mode shape expansion process is not required because an MSR that only includes x- or y-translational degrees of freedom (DoFs) at the measured points is sufficient for detecting damage. The effects of the location and extent of damage, incomplete measurements, and the critical value of the t-test on the performance of the proposed scheme are discussed by numerically analyzing an offshore platform. The results indicate that the scheme can simultaneously limit the probability of committing Type I and Type II errors under temperature variations and noise contamination. An analysis of field test data for a real-world wind turbine also indicates that this approach can avoid Type I error in a real marine environment.

Laser-induced breakdown spectroscopy spectral feature selection to enhance classification capabilities: A t-test filter approach

Extracting classification-relevant information from complex spectral data usually takes extensive expert knowledge and a lot of time and effort. The main challenge remains processing, in a practically feasible manner, huge amounts of highly variable data to recognize patterns useful to explain away classifications. In this paper we present a statistical hypothesis testing approach to select, with minimum knowledge regarding the spectral matrix, the LIBS spectral features potentially most useful to feed supervised classification algorithms. In probing the capabilities of our approach, starting from raw spectral data, a bio-LIBS application was used in an attempt to uncover the chemistry behind the severity diagnosis of liver fibrosis. By coupling our approach with Kernel Density Estimation and Gradient Boosting as normalizing strategy and classification algorithm, respectively, we managed to correctly classify 84% of the spectra, 91% of spots and 95% of samples with error rates comparable to computationally costlier learning-methods such as Random Forest. A definite advantage of the proposed approach is that it is highly scalable, parallelizable, and susceptible to carry-out in a piecemeal fashion.

Statistical testing approach for fractional anomalous diffusion classification.

Taking advantage of recent single-particle tracking data, we compare the popular standard mean-squared displacement method with a statistical testing hypothesis procedure for three testing statistics and for two particle types: membrane receptors and the G proteins coupled to them. Each method results in different classifications. For this reason, more rigorous statistical tests are analyzed here in detail. The main conclusion is that the statistical testing approaches might provide good results even for short trajectories, but none of the proposed methods is "the best" for all considered examples; in other words, one needs to combine different approaches to get a reliable classification.

A Heuristic Statistical Testing Based Approach for Encrypted Network Traffic Identification

In recent years, malware with strong concealment uses encrypted protocol to evade detection. Thus, encrypted traffic identification can help security analysts to be more effective in narrowing down those encrypted network traffic. Existing methods are protocol independent, such as statistical-based and machine-learning-based approaches. Statistical-based approaches, however, are confined to payload length and machine-learning-based approaches have a low recognition rate for encrypted traffic using undisclosed protocols. In this paper, we proposed a heuristic statistical testing (HST) approach that combines both statistics and machine learning and has been proved to alleviate their respective deficiencies. We manually selected four randomness tests to extract small payload features for machine learning to improve real-time performances. We also proposed a simple handshake skipping method called HST-R to increase the classification accuracy. We compared our approach with other identification approaches on a testing dataset consisting of traffic that uses two known, two undisclosed, and one custom cryptographic protocols. Experimental results showed that HST-R performs better than other traditional coding-based, entropy-based, and ML-based approaches. We also showed that our handshake skipping method could generalize better for unknown cryptographic protocols. Finally, we also conducted experimental comparisons among different classification algorithms. The results showed that C4.5, with our method, has the highest identification accuracy for secure sockets layer and secure shell traffic.

A Novel Framework for Learning Automata: A Statistical Hypothesis Testing Approach

A Novel Framework for Learning Automata: A Statistical Hypothesis Testing Approach

Finding characteristics of exceptional breast cancer subpopulations using subgroup mining and statistical test

Breast cancer is one of the most prevalent types of cancer among women. With increased emphasis towards cancer related research, many data-driven research works have been conducted for classifying cancer diagnosis, survival, or recurrence. Unlike existing literature, this study aims to discover interesting subgroup patterns of long-term and short-term survival from the breast cancer incidence data of the SEER (Surveillance, Epidemiology, and End Results) Program. We present a rule induction method for subgroup discovery, which can effectively find subgroup patterns by focusing on local exceptionality detection in contrast to global models. The significance of subgroup patterns discovered is examined with statistical tests. Furthermore the characteristics of two exceptional high and low survival groups are compared by examining the descriptive statistics of prognostic factors in each group. The case study’s results show that the proposed subgroup mining and statistical test approach is a promising technique for clinical and medical data analytics.