Size Of Sample Set Research Articles

Purchase decisions for standard needs at Puri Taman Sukawati company

This study aims to find out and analyze the effect of Customer solutions, Customer cost and Convenience on the psychological consumers of Toko Puri Taman Sukawati, and to find out and analyze the psychological influence of consumers on the purchasing decisions of consumers of Toko Sukawati Park Castle. The population used is customers who have purchased Puritaman basic necessities products domiciled in Sukawati district, the number of which cannot be mentioned or calculated with certainty (infinite). The sampling technique used in this study is purposive sampling technique. The number of sample members or sample size (sample size) set in this study was 95 respondents. Data collection was carried out using questionnaire tools. The analysis technique used is multiple regression analysis.

Wellbeing and mental health interventions for Indigenous children and youth: A systematic scoping review

IntroductionKey policy directives from Canada, Australia, New Zealand, Norway and the United States (CANNZUS countries) highlight the importance of addressing the unique and complex wellbeing and mental health needs of Indigenous school-aged children and youth. This systematic scoping review of the peer reviewed and grey literature identifies the extant evidence about the conditions, strategies and impacts of wellbeing and mental health screening, management and referral pathways for Indigenous children and youth in CANNZUS countries. MethodsA search for peer reviewed and grey literature was conducted of 17 electronic databases and 13 clearinghouses and websites in each of the included countries. Search results were imported into Endnote X9 with titles and abstracts screened against inclusion criteria. The full texts of selected publications were screened by at least two blinded reviewers. Data extracted from the full texts included: authorship, year and publication type; country, target group, sample size and intervention setting, intervention type and study design, outcome measures and reported outcomes. ResultsThe searches produced 3223 peer reviewed and 278 grey literature publications (after duplicates were removed) giving a total of 3501 references. Screening resulted in 15 papers arising from 14 intervention studies in primary health care services, education, juvenile justice and community settings. Only 9 of the 15 publications evaluated an intervention; 6 were program descriptions. The review identified four key approaches to improve wellbeing and mental health care for Indigenous children and youth – cross-service collaboration, professional education, intervention quality and appropriateness, and enhancing direct service provision pathways. Intervention outcomes included acceptability, appropriateness and effectiveness. Discussion/conclusionThe small number of studies found suggests that this research niche area is still in development, with few evaluations of interventions. Identification of interventions within the four broad strategy areas is useful, and the review has some value in guiding the tailored development of interventions in other sites. Further Indigenous-led investigations of wellbeing are needed to identify what works to improve wellbeing and mental healthcare for Indigenous children and youth.

Analysis of distance matrices

The relationships between eigenvalues of the distance matrices and outliers are largely unexplored. We show a discrepancy in the sizes of eigenvalues of the distance matrix that is contaminated with outliers. We present test statistics to determine when a distance matrix has a constant structure. We relate the eigenvalues of the two-sample distance matrix to the tests of equality of distributions, study dissimilarity matrices and their eigenvalues in high dimensional and low sample size setting.

Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.

Nonparametric estimation of some dividend problems in the perturbed compound Poisson model

In this paper, we consider some dividend problems in the perturbed compound Poisson model under a constant barrier dividend strategy. We approximate the expected present value of dividend payments before ruin and the expected discounted penalty function based on the COS method, and construct some nonparametric estimators by using a random sample on claim number and individual claim sizes. Under a large sample size setting, we perform an error analysis of the estimators. We also provide some simulation results to verify the effectiveness of this estimation method when the sample size is finite.

Reassessment of Historical Clinical Trials Supports the Effectiveness of Phage Therapy.

Phage therapy has become a hot topic in medical research due to the increasing prevalence of antibiotic-resistant bacteria strains. In the treatment of bacterial infections, bacteriophages have several advantages over antibiotics, including strain specificity, lack of serious side effects, and low development costs. However, scientists dismissed the clinical success of early clinical trials in the 1940s, slowing the adoption of this promising antibacterial application in Western countries. The current study used statistical methods commonly used in modern meta-analysis to reevaluate early 20th-century studies and compare them with clinical trials conducted in the last 20 years. Using a random effect model, the development of disease after treatment with or without phages was measured in odds ratios (OR) with 95% confidence intervals (CI). Based on the findings of 17 clinical trials conducted between 1921 and 1940, phage therapy was effective (OR = 0.21, 95% CI = 0.10 to 0.44, P value < 0.0001). The current study includes a topic review on modern clinical trials; four could be analyzed, indicating a noneffective therapy (OR = 2.84, 95% CI = 1.53 to 5.27, P value = 0.0009). The results suggest phage therapy was surprisingly less effective than standard treatments in resolving bacterial infections. However, the results were affected by the small sample set size. This work also contextualizes the development of phage therapy in the early 20th century and highlights the expansion of phage applications in the last few years. In conclusion, the current review shows phage therapy is no longer an underestimated tool in the treatment of bacterial infections.

Endogenous Treatment Effect Estimation with a Large and Mixed Set of Instruments and Control Variables

Abstract Instrumental variables (IV) and control variables are frequently used to assist researchers in investigating the endogenous treatment effects. When used together, their identities are typically assumed to be known. However, in many practical situations, one is faced with a large and mixed set of covariates, some of which can serve as excluded IVs, some can serve as control variables, while others should be discarded from the model. It is often not possible to classify them based on economic theory alone. This paper proposes a data-driven method to classify a large (increasing with sample size) set of covariates into excluded IVs, controls, and noise to be discarded. The resulting IV estimator is shown to have the oracle property (to have the same first-order asymptotic distribution as the IV estimator, assuming the true classification is known).

A high dimensional dissimilarity measure

A new dissimilarity measure for high-dimensional, low sample size settings to compare high dimensional probability distributions is proposed. The asymptotic behavior of the new dissimilarity index is studied theoretically. Numerical experiments from high dimensional distributions exhibit the usefulness of the method. The eigenvalues of the matrix of dissimilarities for comparing two high dimensional samples are determined and shown to be related to the asymptotic value of the dissimilarity index. A dissimilarity visualization plot that is useful for detection of outliers and change points is proposed and utilized to find the change points in S&P500 stock return data.

Investigating Performance of Composite Quantile Regression with and without Penalization

The performance of composite quantile regression with and without penalization was investigated and results compared to Lasso Quantile Regression (LQR), Lasso Regression (LR) and Ridge Regression (RR). The mean square error, Akiake information criteria and mean absolute percentage error were used as the comparative criteria. The comparison was illustrated using real dataset and simulated data sets of sample sizes n=30,100,300,500 and 1000. Five consecutive quantiles; 0.19, 0.39, 0.59, 0.79 and 0.99 were used for the quantile regression methods while the lasso regression and ridge regression were based on the mean effect. Another set of quantiles; 0.25, 0.5, 0.75 and 0.95 were also accessed based on only CQR and CQR_AL. The results shows that the composite quantile regression without penalization (CQR) and composite quantile regression with penalization (CQR_AL) achieved same results with the lowest variance on estimated effects and was best fitted model for very large data sets (n=100, 300, 500 &amp; 1000). For the real life data with sample size of 318, the CQR_AL showed the least MSE=421.7653 and AIC=1930.136, but its prediction accuracy was low at 19.03%. Considering sample size 30, the LQR at the 0.59th quantile had the lowest variance with MSE of 1.3940 and was best fitted with AIC of 340.20, also it was noticed that COR_AL achieved the next lowest value for MSE and AIC. These results led to the conclusion that CQR_AL and CQR can be used alternatively with large data sets. But when the sample size is small the LQR is most suited, but if a combined quantile effect is sort the CQR_AL should be opted for.

A machine learning approach to small area estimation: predicting the health, housing and well-being of the population of Netherlands

BackgroundLocal policymakers require information about public health, housing and well-being at small geographical areas. A municipality can for example use this information to organize targeted activities with the aim of improving the well-being of their residents. Surveys are often used to gather data, but many neighborhoods can have only few or even zero respondents. In that case, estimating the status of the local population directly from survey responses is prone to be unreliable.MethodsSmall Area Estimation (SAE) is a technique to provide estimates at small geographical levels with only few or even zero respondents. In classical individual-level SAE, a complex statistical regression model is fitted to the survey responses by using auxiliary administrative data for the population as predictors, the missing responses are then predicted and aggregated to the desired geographical level. In this paper we compare gradient boosted trees (XGBoost), a well-known machine learning technique, to a structured additive regression model (STAR) designed for the specific problem of estimating public health and well-being in the whole population of the Netherlands.ResultsWe compare the accuracy and performance of these models using out-of-sample predictions with five-fold Cross Validation (5CV). We do this for three data sets of different sample sizes and outcome types. Compared to the STAR model, gradient boosted trees are able to improve both the accuracy of the predictions and the total time taken to get these predictions. Even though the models appear quite similar in overall accuracy, the small area predictions at neighborhood level sometimes differ significantly. It may therefore make sense to pursue slightly more accurate models for better predictions into small areas. However, one of the biggest benefits is that XGBoost does not require prior knowledge or model specification. Data preparation and modelling is much easier, since the method automatically handles missing data, non-linear responses, interactions and accounts for spatial correlation structures.ConclusionsIn this paper we provide new nationwide estimates of health, housing and well-being indicators at neighborhood level in the Netherlands, see ’Online materials’. We demonstrate that machine learning provides a good alternative to complex statistical regression modelling for small area estimation in terms of accuracy, robustness, speed and data preparation. These results can be used to make appropriate policy decisions at a local level and make recommendations about which estimation methods are beneficial in terms of accuracy, time and budget constraints.

Estimation of gas turbine technical condition using machine learning methods

This paper considers the method to estimate the technical condition of gas turbine power for natural gas transportation, using machine learning methods. Source data was used to archive gas-dynamic parameters from the automatic control system of the gas turbine. The method is based on changing the enthalpy of the natural gas before and after the centrifugal gas compressor is used for creating a dataset with measured parameters and power from the gas turbine. The actual power is determined from the line of modes for a certain period. The software is implemented using Python and the Scikit-learn library is used to create machine learning models. A mean average percentile error is chosen as the model quality criterion. In this paper, different sets of feature parameters and sample sizes are researched by the quality of the prediction machine learning models. Recommendations on the use of models are given. It has been established that the approach is not applicable for predicting future technical condition without the presence of data on a similar technical condition in the training sample. It is recommended to use the described approach to determine the technical condition in a period of operation in the past.

Determination of the Incidence and Prevalence Rates of Chronic Diseases among Old age at Al-Russafa District in Baghdad City

Objectives: To evaluate the incidence and prevalence rates of chronic diseases among old age at Al-Russafa District in Baghdad City. Methodology: A descriptive design, using the evaluation approach, is employed to determine of the incidence and prevalence rates of chronic diseases among old age at al-Russafa District in Baghdad City from October10th 2021 to April 2nd 2022. A non-probability, convenient sample of (672214) old age, is selected for the purpose of the study. All subjects have signed consent form to present their agreement for participation in the study. The confidentiality of the data is also safeguard and they will be securely maintained during and after conducting the study. A questionnaire is constructed for the purpose of the study through review of related literature. It is consisted of (3) main parts; old age demographic characteristics, old age chronic diseases and the incidence and the prevalence of old age chronic diseases. Content validity is determined for the study instrument through panel of experts and internal consistency reliability is obtained for the study instrument through split-half technique and the computation of Cronbach alpha correlation coefficient in a pilot study. Data are gathered through the use of the study instrument as mean of data collection and analyzed through the application of descriptive statistical data analysis and inferential statistical data analysis approaches. Results: The study results depict that old age chronic diseases are accounted for cardiovascular and chronic respiratory diseases at Al-Russafa District in Baghdad City. Males old age have been prominently at greater risk to the incidence and the prevalence of such diseases more than females at Al-Russafa District in Baghdad City. Conclusion: The study concludes that high incidence rates are accounted for chronic respiratory diseases, cardiovascular diseases, arterial hypertension, kidney diseases, diabetes mellitus and arthritis at Al-Russafa District in Baghdad City and high prevalence rates are accounted for chronic respiratory diseases, cardiovascular diseases, arterial hypertension, kidney diseases and diabetes mellitus at Al-Russafa District in Baghdad City. Recommendations: The study recommends that screening and early detection programs should be planned and implemented on regular base for the finding of such subpopulation-based health problems. Further and nation-wide research can be conducted on large scale sample size and different settings. Keywords: Determination, Incidence Rate, Prevalence Rate, Chronic Diseases, Old age

Ultrasound Evaluation of the Primary α Phase Grain Size Based on Generative Adversarial Network

Because of the high cost of experimental data acquisition, the limited size of the sample set available when conducting tissue structure ultrasound evaluation can cause the evaluation model to have low accuracy. To address such a small-sample problem, the sample set size can be expanded by using virtual samples. In this study, an ultrasound evaluation method for the primary α phase grain size based on the generation of virtual samples by a generative adversarial network (GAN) was developed. TC25 titanium alloy forgings were treated as the research object. Virtual samples were generated by the GAN with a fully connected network of different sizes used as the generator and discriminator. A virtual sample screening mechanism was constructed to obtain the virtual sample set, taking the optimization rate as the validity criterion. Moreover, an ultrasound evaluation optimization problem was constructed with accuracy as the target. It was solved by using support vector machine regression to obtain the final ultrasound evaluation model. A benchmark function was adopted to verify the effectiveness of the method, and a series of experiments and comparison experiments were performed on the ultrasound evaluation model using test samples. The results show that the learning accuracy of the original small samples can be increased by effective virtual samples. The ultrasound evaluation model built based on the proposed method has a higher accuracy and better stability than other models.

Covariance shrinkage can assess and improve functional connectomes

Connectomes derived from resting-state functional MRI scans have significantly benefited from the development of dedicated fMRI motion correction and denoising algorithms. But they are based on empirical correlations that can produce unreliable results in high dimension low sample size settings. A family of statistical estimators, the covariance shrinkage methods, could mitigate this issue. Unfortunately, these methods have rarely been used to correct functional connectomes and no extensive experiment has been conducted so far to compare the shrinkage methods available for this task. In this work, we propose to fix this issue by processing a benchmark dataset made of a thousand high-resolution resting-state fMRI scans provided by the Human Connectome Project to compare the ability of five prominent covariance shrinkage methods to produce reliable functional connectomes at different spatial resolutions and scans duration: the pioneer linear covariance shrinkage method introduced by Ledoit and Wolf, the Oracle Approximating Shrinkage, the QuEST method, the NERCOME method, and a recent analytical approximation of the QuEST approach. Our experiments establish that all covariance shrinkage methods significantly improve functional connectomes derived from short fMRI scans. The Oracle Approximating Shrinkage and the QuEST method produced the best results. Lastly, we present shrinkage intensity charts that can be used for designing and analyzing fMRI studies. These charts indicate that sparse connectomes are difficult to estimate from short fMRI scans, and they describe a range of settings where dynamic functional connectivity should not be computed.

Monitoring diet with automated microhistology

AbstractMicroscopic analysis of indigestible fragments (microhistology) has long been used to monitor diets of wildlife, but the method is slow and difficult to replicate. We automated microhistology analysis by training a computer vision network to quantify the percent slide cover of identified fragments. We compared automated and manual analyses for 3 reference mixes with known proportions of plants and for samples of feces collected during the fall of 2016 and 2017 from white‐tailed deer (Odocoileus virginianus) and goats (Capra aegagrus) with access to the same plants in the wild at the Sonora Research Station, Edward's Plateau, Texas. Consistent size and shape of fragments for grass (Bothriochloa ischaemum) and conifer (Juniperus ashei) were associated with absolute errors of 6 to 16% for both methods. Irregular fragments of leaves and acorns of oak (Quercus virginiana) were underestimated by both methods. Small star‐shaped trichomes of a forb (Croton fruticulosus) were underestimated by computer analysis but overestimated by manual analysis. Fecal fragments from deer were more diverse in plant species and more variable in thickness and shape than those of goats. Computer analysis estimated conifer content of the goat diet within 4% of the manual estimate whereas those estimates for deer were up to 35% greater than the manual estimate. Automated analysis of digital images can increase the rigor of microhistology by expanding the size of sample sets and facilitating the routine use of calibrated references to assess errors in estimates of diet.

Yüksek Boyut Düşük Örneklem Genişliğine Sahip Verilerin Sınıflandırılmasında Random Ferns: Bir Benzetim Çalışması

Objective: Nowadays, 'high dimension low sample size (HDLSS) settings' is very popular in many areas such as genetics, bioinformatics, medical imaging. In this study, it was aimed to investigate the classification performance of Random Ferns, which is a relatively new classifier, on HDLSS settings with different characteristics. Material and Methods: By simulation studies, artificial data sets that have different characteristics in terms of dimension, sample size, correlation structure, noise ratio and prevalence, were generated. Each scenario was iterated for 1,000 times and, classification performances of Random Ferns has been compared with support vector machines (SVM), which stand out with its high classification performance. Results: The performance variation of Random Ferns differs from SVM. It performed better at small sample size (n=20). When the F values are examined, it is seen that the classification performance of Random Ferns in the case of imbalanced distribution does not change much according to the balanced distribution situation and is higher than SVM. It has also been observed that high success as was expected in the classical data structure cannot be achieved. Conclusion: It is noteworthy that Random Ferns outperforms, especially in balanced distribution and small sample sizes. It is thought that this method, which does not have many applications in the field of health, will contribute to studies where the number of observations is quite low.

A Hyperparameter-Free, Fast and Efficient Framework to Detect Clusters From Limited Samples Based on Ultra High-Dimensional Features.

Clustering is a challenging problem in machine learning in which one attempts to group N objects into K0 groups based on P features measured on each object. In this article, we examine the case where N ≪ P and K0 is not known. Clustering in such high dimensional, small sample size settings has numerous applications in biology, medicine, the social sciences, clinical trials, and other scientific and experimental fields. Whereas most existing clustering algorithms either require the number of clusters to be known a priori or are sensitive to the choice of tuning parameters, our method does not require the prior specification of K0 or any tuning parameters. This represents an important advantage for our method because training data are not available in the applications we consider (i.e., in unsupervised learning problems). Without training data, estimating K0 and other hyperparameters-and thus applying alternative clustering algorithms-can be difficult and lead to inaccurate results. Our method is based on a simple transformation of the Gram matrix and application of the strong law of large numbers to the transformed matrix. If the correlation between features decays as the number of features grows, we show that the transformed feature vectors concentrate tightly around their respective cluster expectations in a low-dimensional space. This result simplifies the detection and visualization of the unknown cluster configuration. We illustrate the algorithm by applying it to 32 benchmarked microarray datasets, each containing thousands of genomic features measured on a relatively small number of tissue samples. Compared to 21 other commonly used clustering methods, we find that the proposed algorithm is faster and twice as accurate in determining the "best" cluster configuration.

Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers

Unlike its intercept, a linear classifier&#x0027;s weight vector cannot be tuned by a simple grid search. Hence, this paper proposes weight vector tuning of a generic binary linear classifier through the parameterization of a decomposition of the discriminant by a scalar which controls the trade-off between conflicting informative and noisy terms. By varying this parameter, the original weight vector is modified in a meaningful way. Applying this method to a number of linear classifiers under a variety of data dimensionality and sample size settings reveals that the classification performance loss due to non-optimal native hyperparameters can be compensated for by weight vector tuning. This yields computational savings as the proposed tuning method reduces to tuning a scalar compared to tuning the native hyperparameter, which may involve repeated weight vector generation along with its burden of optimization, dimensionality reduction, etc., depending on the classifier. It is also found that weight vector tuning significantly improves the performance of Linear Discriminant Analysis (LDA) under high estimation noise. Proceeding from this second finding, an asymptotic study of the misclassification probability of the parameterized LDA classifier in the growth regime where the data dimensionality and sample size are comparable is conducted. Using random matrix theory, the misclassification probability is shown to converge to a quantity that is a function of the true statistics of the data. Additionally, an estimator of the misclassification probability is derived. Finally, computationally efficient tuning of the parameter using this estimator is demonstrated on real data.

Fully corrective gradient boosting with squared hinge: Fast learning rates and early stopping

In this paper, we propose an efficient boosting method with theoretical guarantees for binary classification. There are three key ingredients of the proposed boosting method: a fully corrective greedy (FCG) update, a differentiable squared hinge (also called truncated quadratic) loss function, and an efficient alternating direction method of multipliers (ADMM) solver. Compared with traditional boosting methods, on one hand, the FCG update accelerates the numerical convergence rate, and on the other hand, the squared hinge loss inherits the robustness of the hinge loss for classification and maintains the theoretical benefits of the square loss in regression. The ADMM solver with guaranteed fast convergence then provides an efficient implementation for the proposed boosting method. We conduct both theoretical analysis and numerical verification to show the outperformance of the proposed method. Theoretically, a fast learning rate of order O((m/logm)−1/2) is proved under certain standard assumptions, where m is the size of sample set. Numerically, a series of toy simulations and real data experiments are carried out to verify the developed theory.

Oscillatory Source Tensor Discriminant Analysis (OSTDA): A regularized tensor pipeline for SSVEP-based BCI systems

Periodic signals called Steady-State Visual Evoked Potentials (SSVEP) are elicited in the brain by flickering stimuli. They are usually detected by means of regression techniques that need relatively long trial lengths to provide feedback and/or sufficient number of calibration trials to be reliably estimated in the context of brain-computer interface (BCI). Thus, for BCI systems designed to operate with SSVEP signals, reliability is achieved at the expense of speed or extra recording time. Furthermore, regardless of the trial length, calibration free regression-based methods have been shown to suffer from significant performance drops when cognitive perturbations are present affecting the attention to the flickering stimuli. In this study we present a novel technique called Oscillatory Source Tensor Discriminant Analysis (OSTDA) that extracts oscillatory sources and classifies them using the newly developed tensor-based discriminant analysis with shrinkage. The proposed approach is robust for small sample size settings where only a few calibration trials are available. Besides, it works well with both low- and high-number-of-channel settings, using trials as short as one second. OSTDA performs similarly or significantly better than other three benchmarked state-of-the-art techniques under different experimental settings, including those with cognitive disturbances (i.e. four datasets with control, listening, speaking and thinking conditions). Overall, in this paper we show that OSTDA is the only pipeline among all the studied ones that can achieve optimal results in all analyzed conditions.