Regression analysis is a popular tool used in data analysis, whereas fuzzy regression is usually used for analyzing uncertain and imprecise data. In the industrial area, the company usually has problems in predicting the future manufacturing income. Therefore, a new approach model is needed to solve the future company prediction income. This article analyzed the manufacturing income by using the multiple linear regression (MLR) model and fuzzy linear regression (FLR) model proposed by Tanaka and Zolfaghari, involving 9 explanatory variables. In order to find the optimum of the FLR model, the degree of fitting (H) was adjusted between 0 to 1. The performance of three methods has been measured by using mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The analysis proved that FLR with Zolfaghari’s model with the degree of fitting of 0.025 outperformed the MLR and FLR with Tanaka’s model with the smallest error value. In conclusion, the manufacturing income is directly proportional to 6 independent variables. Furthermore, the manufacturing income is inversely proportional to 3 independent variables. This model is suitable in predicting future manufacturing income.

In this study, we are aiming to estimate the stress-strength reliability, R=P(Y

This study provides a time series analysis and interpretation of the output for forecast sales of chicken based food product of weekly sales data. These data were collected directly from the outlet shop of one factory in Malacca started from January 2015 to December 2016. Methods of forecasting include autoregressive (AR) method and simple exponential smoothing (SES) method. The accuracy for both methods will be compared using mean squared error (MSE), mean absolute percentage error (MAPE) and mean absolute deviation (MAD). There will be 1 period ahead of predictions for AR method and 1 period ahead for SES method. This analysis found that AR method with AR (1) model is more accurate than SES method and can be used for the future prediction of chicken based food product of weekly sales data. Recommendations for future study is trying out other method to analyse this sales of chicken based food product and using R software to analyse the dataset.

Bu çalışmada, Burr Tipi III Dağılım’a ait bilinmeyen parametrelerinin istatistiksel tahmini, ilerleyen tür tip II sağdan sansürlü örneklem durumunda incelenmiştir. Bilinmeyen parametrelerin en çok olabilirlik tahmin edicileri Newton-Raphson yöntemi ve Beklenti Maksimizasyonu (EM) algoritması kullanılarak elde edilmiş, parametrelerin asimptotik güven aralıkları kayıp değer prensibine dayalı Fisher bilgi matrisi aracılığıyla bulunmuştur. Tahmin edicilerin performansları farklı sansür şemaları ve parametre değerleri için benzetim çalışması yoluyla karşılaştırılmıştır. Ayrıca çalışmanın daha açıklayıcı olması amacıyla gerçek bir yaşam verisi örneği de verilmiştir.

Abstract Confidence intervals (CIs) play fundamental task in statistical inferences and statsitical decision making. For this sake, many researches have been conducted to propose optimal CIs. One method to construct an optimal CI is use the optimal estimators. In this paper, we consider a new method to construct uniformly better confidence intervals for the scale parameter of the exponential distribution based on record data. In this regard, we use the maximum posterior coverage probability to obtain a new Bayes and Empirical Bayes confidence intervals. The results presented by the use of simulated data and a real data analysis.

Bu çalışmada, genelleştirilmiş üstel dağılıma ait bilinmeyen parametrelerinin istatistiksel tahmini ilerleyen tür tip II sağdan sansürlü örneklem durumunda incelenmiştir. Bilinmeyen parametrelerin en çok olabilirlik tahmin edicileri Newton-Raphson ve Beklenti Maksimizasyonu (EM) algoritması kullanılarak elde edilmiş, parametrelerin asimptotik güven aralıkları kayıp değer prensibine dayalı Fisher bilgi matrisi aracılığıyla bulunmuştur. Tahmin edicilerin performansları farklı sansür şemaları ve parametre değerleri için benzetim çalışması yoluyla karşılaştırılmıştır. Ayrıca çalışmanın daha açıklayıcı olması amacıyla bir gerçek yaşam verisi örneği de verilmiştir.

Background Study-level meta-analyses provide high-certainty evidence that heparin reduces the risk of symptomatic venous thromboembolism for patients with cancer; however, whether the benefits and harms associated with heparin differ by cancer type is unclear. This individual participant data meta-analysis of randomised controlled trials examines the effect of heparin on survival, venous thromboembolism, and bleeding in patients with cancer in general and by type. Methods In this systematic review and meta-analysis we searched MEDLINE, Embase, and The Cochrane Library for randomised controlled trials comparing parenteral anticoagulants with placebo or standard care in ambulatory patients with solid tumours and no indication for anticoagulation published from the inception of each database to January 14, 2017, and updated it on May 14, 2020, without language restrictions. We calculated the effect of parenteral anticoagulant administration on all-cause mortality, venous thromboembolism occurrence, and bleeding related outcomes through multivariable hierarchical models with patient-level variables as fixed effects and a categorical trial variable as a random effect, adjusting for age, cancer type, and metastatic status. Interaction terms were tested to investigate effects in predefined subgroups. This study is registered with PROSPERO, CRD42013003526. Findings We obtained individual participant data from 14 of 20 eligible randomised controlled trials (8278 [79%] of 10 431 participants; 4139 included in the low-molecular-weight heparin group and 4139 in the control group). Meta-analysis showed an adjusted relative risk (RR) of mortality at 1 year of 0.99 (95% CI 0.93-1.06) and a hazard ratio of 1.01 (95% CI 0.96-1.07). The number of patients with venous thromboembolic events was 158 (4.0%) of 3958 with available data in the low-molecular-weight heparin group compared with 279 (7.1%) of 3957 in the control group. Major bleeding events occurred in 71 (1.7%) of 4139 patients in the control population and 88 (2.1%) in the low-molecular-weight heparin group, and minor bleeding events in 478 (12.1%) of 3945 patients with available data in the control group and 652 (16.6%) of 3937 patients in the low-molecular-weight heparin group. The adjusted RR was 0.58 (95% CI 0.47-0.71) for venous thromboembolism, 1.27 (0.92-1.74) for major bleeding, and 1.34 (1.19-1.51) for minor bleeding. Prespecified subgroup analysis of venous thromboembolism occurrence by cancer type identified the most certain benefit from heparin treatment in patients with lung cancer (RR 0.59 [95% CI 0.42-0.81]), which dominated the overall reduction in venous thromboembolism. Certainty of the evidence for the outcomes ranged from moderate to high. Interpretation Low-molecular-weight heparin reduces risk of venous thromboembolism without increasing risk of major bleeding compared with placebo or standard care in patients with solid tumours, but it does not improve survival. Copyright (C) 2020 Elsevier Ltd. All rights reserved.

Complementary exponential geometric distribution has many applications in survival and reliability analysis. Due to its importance, in this study, we are aiming to estimate the parameters of this model based on progressive type-II censored observations. To do this, we applied the stochastic expectation maximization method and Newton–Raphson techniques for obtaining the maximum likelihood estimates. We also considered the estimation based on Bayesian method using several approximate: MCMC samples, Lindely approximation and Metropolis–Hasting algorithm. In addition, we considered the shrinkage estimators based on Bayesian and maximum likelihood estimators. Then, the HPD intervals for the parameters are constructed based on the posterior samples from the Metropolis–Hasting algorithm. In the sequel, we obtained the performance of different estimators in terms of biases, estimated risks and Pitman closeness via Monte Carlo simulation study. This paper will be ended up with a real data set example for illustration of our purpose.

Shrinkage methods for linear regression were developed over the last ten years to reduce the weakness of ordinary least squares (OLS) regression with respect to prediction accuracy. And, high dimensional data are quickly growing in many areas due to the development of technological advances which helps collect data with a large number of variables. In this paper, shrinkage methods were used to evaluate regression coefficients effectively for the high-dimensional multiple regression model, where there were fewer samples than predictors. Also, regularization approaches have become the methods of choice for analyzing such high dimensional data. We used three regulation methods based on penalized regression to select the appropriate model. Lasso, Ridge and Elastic Net have desirable features; they can simultaneously perform the regulation and selection of appropriate predictor variables and estimate their effects. Here, we compared the performance of three regular linear regression methods using cross-validation method to reach the optimal point. Prediction accuracy using the least squares error (MSE) was evaluated. Through conducting a simulation study and studying real data, we found that all three methods are capable to produce appropriate models. The Elastic Net has better prediction accuracy than the rest. However, in the simulation study, the Elastic Net outperformed other two methods and showed a less value in terms of MSE.

The aim of this paper is to discuss the estimation and prediction problems for the Burr type-III distribution under progressive type-II hybrid censored data. We obtained maximum likelihood estimators (MLEs) of unknown parameters using stochastic expectation maximization (SEM) algorithms, and the asymptotic variance–covariance matrix of the MLEs under SEM framework is obtained by Fisher’s information matrix. We provide various Bayes estimators for unknown parameters using Lindley’s approximation method and importance sampling technique from square error, entropy, and linex loss functions. Finally, we analyze a real data set and generate a simulation study to compare the performance of various proposed estimators and predictors under different situations.

Prediction of stock market value is one the most complicated issue during the past decades. Due to its importance, in this research, we consider the prediction of stock values based on non-parametric and parametric methods. In this first method, we use the fuzzy Markov chain procedure in order to prediction problem. In this regard, all of the rising and falling probabilities during the weekdays are calculated and then they applied to obtain the increasing and decreasing rate. Then, based on this information we model and predict the stock values. In the sequel, we implement different methods of parametric time series such as generalized autoregressive conditionally heteroskedastic (GARCH), ARIMA-GARCH, Exponential GARCH (E-GARCH) and GJR-GARCH by assuming the normal and t-student distribution for the error terms to obtain the best model in terms of minimum mean square errors. Finally, the mythologies developed here are applied for the Tehran Stock Exchange Index (TEDPIX).

SYNOPTIC ABSTRACT This article addresses the problems of estimation and prediction when the lifetime data following Poisson-Exponential distribution are observed under type-II censoring. We obtain maximum likelihood estimates and associated interval estimates under a classical approach, and Bayes estimates using various loss functions and associated highest posterior density interval estimates. Maximum likelihood estimates are obtained using the Newton-Raphson method and Expectation Maximization (EM) algorithm, and Bayes estimates are computed using importance sampling and Lindley approximation. We also compute shrinkage preliminary test estimates based on maximum likelihood and Bayes estimates. Further, we provide inference on the censored observations by making use of best unbiased and condition median predictors under a classical approach, and predictive estimates under the Bayesian paradigm using importance sampling. The associated predictive interval estimates are also obtained using different methods. Finally, we conduct a simulation study to compare the performance of all the proposed methods of estimation and prediction, and analyze a real data set for illustration purpose.

Introduction Parenteral anticoagulants may improve outcomes in patients with cancer by reducing risk of venous thromboembolic disease and through a direct antitumour effect. Study-level systematic reviews indicate a reduction in venous thromboembolism and provide moderate confidence that a small survival benefit exists. It remains unclear if any patient subgroups experience potential benefits. Methods and analysis First, we will perform a comprehensive systematic search of MEDLINE, EMBASE and The Cochrane Library, hand search scientific conference abstracts and check clinical trials registries for randomised control trials of participants with solid cancers who are administered parenteral anticoagulants. We anticipate identifying at least 15 trials, exceeding 9000 participants. Second, we will perform an individual participant data meta-analysis to explore the magnitude of survival benefit and address whether subgroups of patients are more likely to benefit from parenteral anticoagulants. All analyses will follow the intention-to-treat principle. For our primary outcome, mortality, we will use multivariable hierarchical models with patient-level variables as fixed effects and a categorical trial variable as a random effect. We will adjust analysis for important prognostic characteristics. To investigate whether intervention effects vary by predefined subgroups of patients, we will test interaction terms in the statistical model. Furthermore, we will develop a risk-prediction model for venous thromboembolism, with a focus on control patients of randomised trials. Ethics and dissemination Aside from maintaining participant anonymity, there are no major ethical concerns. This will be the first individual participant data meta-analysis addressing heparin use among patients with cancer and will directly influence recommendations in clinical practice guidelines. Major cancer guideline development organisations will use eventual results to inform their guideline recommendations. Several knowledge users will disseminate results through presentations at clinical rounds as well as national and international conferences. We will prepare an evidence brief and facilitate dialogue to engage policymakers and stakeholders in acting on findings. Trial registration number PROSPERO CRD42013003526.

A progressive hybrid censoring scheme is a mixture of type-I and type-II progressive censoring schemes. In this paper, we mainly consider the analysis of progressive type-II hybrid-censored data when the lifetime distribution of the individual item is the normal and extreme value distributions. Since the maximum likelihood estimators (MLEs) of these parameters cannot be obtained in the closed form, we propose to use the expectation and maximization (EM) algorithm to compute the MLEs. Also, the Newton–Raphson method is used to estimate the model parameters. The asymptotic variance–covariance matrix of the MLEs under EM framework is obtained by Fisher information matrix using the missing information and asymptotic confidence intervals for the parameters are then constructed. This study will end up with comparing the two methods of estimation and the asymptotic confidence intervals of coverage probabilities corresponding to the missing information principle and the observed information matrix through a simulation study, illustrated examples and real data analysis.

This paper considers the role of influence diagnostics in the partially linear regression models, y = Xβ + f + ε .An influential observation on the estimator of the coefficient vector  may not be influential on that of the nonparametric component f(x), and vice versa.Also, an observation which is not influential on either parametric or non-parametric component may be influential on the estimator of the mean response.So, we focus on influence measures for each estimator β , f , and the mean response Xβ + f .In the literature, the Cook's distance is used to detect influential observation in partially linear models.In certain types of data sets, it is quite common an unusual observation or a small subset using Dffits, Dfbetas, and CovRatio statistics.Therefore, in our study, Dffits, Dfbetas, and CovRatio are proposed to identify any influential observation in the partially linear regression models.These measures are discussed on each of which measures the effect of detecting an influential observation by using real and simulation data sets.

This study presents two different kinds of preliminary test estimators based on Type II censoredobservations in the two parameters exponential model. We deSne MLE and MRE preliminary test estimatorsin the same fashion as in the ordinary preliminary test estimator using relevant combinations of MLE andMRE estimators. Exact bias and MSE expressions for the proposed estimators are derived . We compare theMSEs and obtain some intervals for the parameter of interest in which the preliminary test type estimatorsoutperforms the MLE and MRE estimators. Some graphical representations are given for the illustrationpurpose. Finally, we conclude this approach by a useful discussion for practical purposes and a summary

Multiple linear regression models are widely used in applied statistical techniques and they are most useful devices for extracting and understanding the essential features of datasets. However, in multiple linear regression models, problems arise when multicollinearity or a serious outlier observation present in the data. Multicollinearity is a linear dependency between two or more explanatory variables in the regression models which can seriously affect the least squares estimated regression surface. The other important problem is outlier; they can strongly influence the estimated model, especially when using least squares method. Nevertheless, outlier data are often the special points of interests in many practical situations. The purpose of this study is to performance comparison of Akaike Information Criterion (AIC’), Bayesian Information Criterion (BIC’) and Information Complexity Criterion (ICOMP’(IFIM)) for detecting outliers using Genetic Algorithms when multiple regression model having multicollinearity problems.

Background: Spontaneous pneumothoraxes constituted 1/1000 hospital admissions. They are particularly one of life threatening health issues in combination with bilateral pneumothorax, tension pneumothorax, repertory failure or COPD.Objectives: The cases of spontaneous pneumothorax represent a significant portion of the patients profile within the chest surgery clinics. The risk of recurrent pneumothorax in post thoracoscopy is between 2% and 14%, thus the subject of cure treatment and approach is still controversial. The cases were retrospectively treated due to spontaneous pneumothorax and their reasons, treatment approaches and results were comparatively examined with the literature.Patients and Methods: The years between 2007 and 2010, according to our hospital clinic, outpatients and accident & amp; emergency admission records, 79 patients were admitted with a diagnosis of spontaneous pneumothorax; and the patients' age, gender, symptoms, types of pneumothorax, surgical intervention and recurrence, average length of stay, mortality and complications were retrospectively evaluated.Results: Seventy of all the patients (88.6%) were male and 9 of those (13.7%) were female. The mean age was calculated as 45.50 +/- 21.07 (0-85). The patients were comprised of 41 (51.9%) with primary spontaneous pneumothorax and 38 (48.1%) with secondary spontaneous pneumothorax. 55 of the patients (69.6%) with the first attack, and 24 patients (30.4%) with post tube thoracotomy's 2nd or 3rd pneumothorax attack were admitted. Those who were accepted with post tube thoracostomy's 2nd or 3rd attack made up 2/3 of the secondary spontaneous pneumothorax patients. 57 of the patients (68.4%) were treated with the tube thoracostomy. The tube thoracostomy related complication was 6.3%, hemorrhage due to parenchymal damage and massive air leak were observed. An open surgical method to 22 of those patients and apical resection and apical pleurectomy + tetracycline pleurodesis to 16 of whom and bullae ligation and mechanical abrasion to 6 patients were applied. The recurrence of pneumothorax in post-surgery was not observed for 1-3 year period Complication was not detected. Mortality, one patient (1.3%) died in post tube thoracotomy, which was a stage 4 lung cancer patient.Conclusions: Most cases for pneumothorax were consisted of the patients with the primary spontaneous pneumothorax; the patients with recurrent pneumothorax were comprised of secondary spontaneous pneumothorax patients and those of majority secondary spontaneous pneumothorax patients were observed with bullous emphysema profile. By looking at the pertinent literature, there are publications showing VATS with the recurrence rate ranging from 2% to 14% and post thoracotomy recurrence rate from 0% to 7%. We think that applying pleurectomy, mechanical abrasion and chemical pleurodesis additional to bullae ligation or apical resection in pneumothorax surgery will significantly reduce the recurrence rate.