scholarly journals Some Statistical Properties of a Weighted Distribution and Its Application to Waiting Time Data

2020 ◽  
pp. 1-12
Author(s):  
Brijesh P. Singh ◽  
Utpal Dhar Das
Keyword(s):  
Generating Function ◽  
Waiting Time ◽  
Medical Science ◽  
Real Life ◽  
Estimation Method ◽  
Real Data ◽  
Statistical Properties ◽  
Residual Entropy ◽  
Time Data ◽  
Weighted Distribution

The weighted distribution is mainly used in various real life fields such as ecology, reliability engineering, medical science etc. Some statistical properties are derived such as moment generating function, characteristic function, cumulative generating function, the hazard function, Renyi entropy, Cumulative residual entropy. For parameter estimation maximum likelihood estimation method is used. The considered weighted probability distribution is applied to two real data sets of waiting time to examine the suitability and applicability.

scholarly journals The modified beta transmuted family of distributions with applications using the exponential distribution

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258512
Author(s):  
Phillip Oluwatobi Awodutire ◽  
Oluwafemi Samson Balogun ◽  
Akintayo Kehinde Olapade ◽  
Ethelbert Chinaka Nduka
Keyword(s):  
Exponential Distribution ◽  
Real Life ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Life Time ◽  
Time Data ◽  
New Family ◽  
Special Cases ◽  
The Family ◽  
Family Of Distributions

In this work, a new family of distributions, which extends the Beta transmuted family, was obtained, called the Modified Beta Transmuted Family of distribution. This derived family has the Beta Family of Distribution and the Transmuted family of distribution as subfamilies. The Modified beta transmuted frechet, modified beta transmuted exponential, modified beta transmuted gompertz and modified beta transmuted lindley were obtained as special cases. The analytical expressions were studied for some statistical properties of the derived family of distribution which includes the moments, moments generating function and order statistics. The estimates of the parameters of the family were obtained using the maximum likelihood estimation method. Using the exponential distribution as a baseline for the family distribution, the resulting distribution (modified beta transmuted exponential distribution) was studied and its properties. The modified beta transmuted exponential distribution was applied to a real life time data to assess its flexibility in which the results shows a better fit when compared to some competitive models.

scholarly journals The Iwok-Nwikpe Distribution: Statistical Properties and Its Application

2021 ◽  
pp. 35-45
Author(s):  
Iwok Iberedem Aniefiok ◽  
Barinaadaa John Nwikpe
Keyword(s):  
Probability Distribution ◽  
Goodness Of Fit ◽  
Survival Function ◽  
Medical Science ◽  
Mathematical Expression ◽  
Real Data ◽  
Moment Generating Function ◽  
Statistical Properties ◽  
Cumulative Distribution ◽  
Data Sets

In this paper, a new continuous probability distribution named Iwok-Nwikpe distribution is proposed. Some essential statistical properties of the proposed probability distribution have been derived. The graphs of the survival function, probability density function (p.d.f) and cumulative distribution function (c.d.f) were plotted at different values of the parameter. The mathematical expression for the moment generating function (mgf) was derived. Consequently, the first three crude moments were obtained; the distribution of order statistics, the second and third moments corrected for the mean have also been derived. The parameter of the Iwok-Nwikpe distribution was estimated by means of maximum likelihood technique. To establish the goodness of fit of the Iwok-Nwikpe distribution, three real data sets from engineering and medical science were fitted to the distribution. Findings of the study revealed that the Iwok-Nwikpe distribution performed better than the one parameter exponential distribution and other competing models used for the study.

scholarly journals A New Parametric Life Family of Distributions: Properties, Copula and Modeling Failure and Service Times

Symmetry ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 1462
Author(s):  
Mansour Shrahili ◽  
Naif Alotaibi
Keyword(s):  
Maximum Likelihood ◽  
Probability Distributions ◽  
Real Life ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Likelihood Method ◽  
Model Parameters ◽  
New Family ◽  
Renyi’S Entropy

A new family of probability distributions is defined and applied for modeling symmetric real-life datasets. Some new bivariate type G families using Farlie–Gumbel–Morgenstern copula, modified Farlie–Gumbel–Morgenstern copula, Clayton copula and Renyi’s entropy copula are derived. Moreover, some of its statistical properties are presented and studied. Next, the maximum likelihood estimation method is used. A graphical assessment based on biases and mean squared errors is introduced. Based on this assessment, the maximum likelihood method performs well and can be used for estimating the model parameters. Finally, two symmetric real-life applications to illustrate the importance and flexibility of the new family are proposed. The symmetricity of the real data is proved nonparametrically using the kernel density estimation method.

Ambulatory Assessment

2009 ◽  
Vol 14 (2) ◽  
pp. 109-119 ◽  
Author(s):  
Ulrich W. Ebner-Priemer ◽  
Timothy J. Trull
Keyword(s):  
Real Time ◽  
Clinical Psychology ◽  
Real Life ◽  
Ambulatory Assessment ◽  
Computer Assisted ◽  
Affective Valence ◽  
Time Data ◽  
Physiological Processes ◽  
Context Specific ◽  
Questionnaire Studies

Convergent experimental data, autobiographical studies, and investigations on daily life have all demonstrated that gathering information retrospectively is a highly dubious methodology. Retrospection is subject to multiple systematic distortions (i.e., affective valence effect, mood congruent memory effect, duration neglect; peak end rule) as it is based on (often biased) storage and recollection of memories of the original experience or the behavior that are of interest. The method of choice to circumvent these biases is the use of electronic diaries to collect self-reported symptoms, behaviors, or physiological processes in real time. Different terms have been used for this kind of methodology: ambulatory assessment, ecological momentary assessment, experience sampling method, and real-time data capture. Even though the terms differ, they have in common the use of computer-assisted methodology to assess self-reported symptoms, behaviors, or physiological processes, while the participant undergoes normal daily activities. In this review we discuss the main features and advantages of ambulatory assessment regarding clinical psychology and psychiatry: (a) the use of realtime assessment to circumvent biased recollection, (b) assessment in real life to enhance generalizability, (c) repeated assessment to investigate within person processes, (d) multimodal assessment, including psychological, physiological and behavioral data, (e) the opportunity to assess and investigate context-specific relationships, and (f) the possibility of giving feedback in real time. Using prototypic examples from the literature of clinical psychology and psychiatry, we demonstrate that ambulatory assessment can answer specific research questions better than laboratory or questionnaire studies.

scholarly journals AnANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions

2021 ◽  
Vol 11 (11) ◽  
pp. 4757
Author(s):  
Aleksandra Bączkiewicz ◽  
Jarosław Wątróbski ◽  
Wojciech Sałabun ◽  
Joanna Kołodziejczyk
Keyword(s):  
Atmospheric Pressure ◽  
Real Life ◽  
Real Data ◽  
Weather Conditions ◽  
Maximum Temperature ◽  
Air Masses ◽  
Weather Forecasts ◽  
Life Problems ◽  
The World ◽  
The City

Artificial Neural Networks (ANNs) have proven to be a powerful tool for solving a wide variety of real-life problems. The possibility of using them for forecasting phenomena occurring in nature, especially weather indicators, has been widely discussed. However, the various areas of the world differ in terms of their difficulty and ability in preparing accurate weather forecasts. Poland lies in a zone with a moderate transition climate, which is characterized by seasonality and the inflow of many types of air masses from different directions, which, combined with the compound terrain, causes climate variability and makes it difficult to accurately predict the weather. For this reason, it is necessary to adapt the model to the prediction of weather conditions and verify its effectiveness on real data. The principal aim of this study is to present the use of a regressive model based on a unidirectional multilayer neural network, also called a Multilayer Perceptron (MLP), to predict selected weather indicators for the city of Szczecin in Poland. The forecast of the model we implemented was effective in determining the daily parameters at 96% compliance with the actual measurements for the prediction of the minimum and maximum temperature for the next day and 83.27% for the prediction of atmospheric pressure.

scholarly journals The Flexible Burr X-G Family: Properties, Inference, and Applications in Engineering Science

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 474
Author(s):  
Abdulhakim A. Al-Babtain ◽  
Ibrahim Elbatal ◽  
Hazem Al-Mofleh ◽  
Ahmed M. Gemeay ◽  
Ahmed Z. Afify ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Exponential Distribution ◽  
Real Data ◽  
Exponential Model ◽  
Statistical Properties ◽  
Engineering Science ◽  
Data Sets ◽  
Engineering Sciences ◽  
General Statistical ◽  
Anderson Darling

In this paper, we introduce a new flexible generator of continuous distributions called the transmuted Burr X-G (TBX-G) family to extend and increase the flexibility of the Burr X generator. The general statistical properties of the TBX-G family are calculated. One special sub-model, TBX-exponential distribution, is studied in detail. We discuss eight estimation approaches to estimating the TBX-exponential parameters, and numerical simulations are conducted to compare the suggested approaches based on partial and overall ranks. Based on our study, the Anderson–Darling estimators are recommended to estimate the TBX-exponential parameters. Using two skewed real data sets from the engineering sciences, we illustrate the importance and flexibility of the TBX-exponential model compared with other existing competing distributions.

493 Evaluation of Patient Waiting Times for Plain Radiographs Whilst Attending Outpatient Clinics in A Tertiary Orthopaedic Centre

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Z Hayat ◽  
E Kinene ◽  
S Molloy
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Time Frame ◽  
Outpatient Clinics ◽  
Contributing Factors ◽  
Time Data ◽  
Work Related ◽  
Mean Waiting Time ◽  
Appointment Time ◽  
Related Stress

Abstract Introduction Reduction of waiting times is key to delivering high quality, efficient health care. Delays experienced by patients requiring radiographs in orthopaedic outpatient clinics are well recognised. Method To establish current patient and staff satisfaction, questionnaires were circulated over a two-week period. Waiting time data was retrospectively collected including appointment time, arrival time and the time at which radiographs were taken. Results 84% (n = 16) of radiographers believed patients would be dissatisfied. However, of the 296 patients questioned, 56% (n = 165) were satisfied. Most patients (89%) felt the waiting time should be under 30 minutes. Only 36% were seen in this time frame. There was moderate negative correlation (R=-0.5); higher waiting times led to increased dissatisfaction. Mean waiting time was 00:37 and the maximum 02:48. Key contributing factors included volume of patients, staff shortages (73.7%), equipment shortages (57.9%) and incorrectly filled request forms. Eight (42.1%) had felt unwell from work related stress. Conclusions A concerted effort is needed to improve staff and patient opinion. There is scope for change post COVID. Additional training and exploring ways to avoid overburdening the department would benefit. Numerous patients were open to different days or alternative sites. Funding requirements make updating equipment, expanding the department and recruiting more staff challenging.

scholarly journals Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Mathematics ◽  
2021 ◽  
Vol 9 (14) ◽  
pp. 1679
Author(s):  
Jacopo Giacomelli ◽  
Luca Passalacqua
Keyword(s):  
Time Series ◽  
Default Risk ◽  
Real Life ◽  
Statistical Error ◽  
Real Data ◽  
Sampling Period ◽  
Calibration Procedure ◽  
Industry Standards ◽  
Default Rate

The CreditRisk+ model is one of the industry standards for the valuation of default risk in credit loans portfolios. The calibration of CreditRisk+ requires, inter alia, the specification of the parameters describing the structure of dependence among default events. This work addresses the calibration of these parameters. In particular, we study the dependence of the calibration procedure on the sampling period of the default rate time series, that might be different from the time horizon onto which the model is used for forecasting, as it is often the case in real life applications. The case of autocorrelated time series and the role of the statistical error as a function of the time series period are also discussed. The findings of the proposed calibration technique are illustrated with the support of an application to real data.

scholarly journals Improving the performance of a radio-frequency localization system in adverse outdoor applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Marcelo N. de Sousa ◽  
Ricardo Sant’Ana ◽  
Rigel P. Fernandes ◽  
Julio Cesar Duarte ◽  
José A. Apolinário ◽  
...  
Keyword(s):  
Random Forest ◽  
Ray Tracing ◽  
Real World ◽  
Practical Implication ◽  
Real Life ◽  
Simulated Data ◽  
Real Data ◽  
Gradient Boosting ◽  
Real World Data ◽  
Localization Accuracy

AbstractIn outdoor RF localization systems, particularly where line of sight can not be guaranteed or where multipath effects are severe, information about the terrain may improve the position estimate’s performance. Given the difficulties in obtaining real data, a ray-tracing fingerprint is a viable option. Nevertheless, although presenting good simulation results, the performance of systems trained with simulated features only suffer degradation when employed to process real-life data. This work intends to improve the localization accuracy when using ray-tracing fingerprints and a few field data obtained from an adverse environment where a large number of measurements is not an option. We employ a machine learning (ML) algorithm to explore the multipath information. We selected algorithms random forest and gradient boosting; both considered efficient tools in the literature. In a strict simulation scenario (simulated data for training, validating, and testing), we obtained the same good results found in the literature (error around 2 m). In a real-world system (simulated data for training, real data for validating and testing), both ML algorithms resulted in a mean positioning error around 100 ,m. We have also obtained experimental results for noisy (artificially added Gaussian noise) and mismatched (with a null subset of) features. From the simulations carried out in this work, our study revealed that enhancing the ML model with a few real-world data improves localization’s overall performance. From the machine ML algorithms employed herein, we also observed that, under noisy conditions, the random forest algorithm achieved a slightly better result than the gradient boosting algorithm. However, they achieved similar results in a mismatch experiment. This work’s practical implication is that multipath information, once rejected in old localization techniques, now represents a significant source of information whenever we have prior knowledge to train the ML algorithm.

scholarly journals A multidimensional generalized many-facet Rasch model for rubric-based performance assessment

2021 ◽  
Author(s):  
Masaki Uto
Keyword(s):  
Performance Assessment ◽  
Rasch Model ◽  
Measurement Accuracy ◽  
Estimation Method ◽  
Real Data ◽  
Monte Carlo Algorithm ◽  
Irt Model ◽  
Irt Models ◽  
Proposed Model ◽  
Problem Item

AbstractPerformance assessment, in which human raters assess examinee performance in a practical task, often involves the use of a scoring rubric consisting of multiple evaluation items to increase the objectivity of evaluation. However, even when using a rubric, assigned scores are known to depend on characteristics of the rubric’s evaluation items and the raters, thus decreasing ability measurement accuracy. To resolve this problem, item response theory (IRT) models that can estimate examinee ability while considering the effects of these characteristics have been proposed. These IRT models assume unidimensionality, meaning that a rubric measures one latent ability. In practice, however, this assumption might not be satisfied because a rubric’s evaluation items are often designed to measure multiple sub-abilities that constitute a targeted ability. To address this issue, this study proposes a multidimensional IRT model for rubric-based performance assessment. Specifically, the proposed model is formulated as a multidimensional extension of a generalized many-facet Rasch model. Moreover, a No-U-Turn variant of the Hamiltonian Markov chain Monte Carlo algorithm is adopted as a parameter estimation method for the proposed model. The proposed model is useful not only for improving the ability measurement accuracy, but also for detailed analysis of rubric quality and rubric construct validity. The study demonstrates the effectiveness of the proposed model through simulation experiments and application to real data.

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