The vast use of Linear Prediction Coefficients (LPC) in speech processing systems has intensified the importance of their accurate computation. This paper is concerned with computing LPC coefficients using evolutionary algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Dif-ferential Evolution (DE) and Particle Swarm Optimization with Differentially perturbed Velocity (PSO-DV). In this method, evolutionary algorithms try to find the LPC coefficients which can predict the origi-nal signal with minimum prediction error. To this end, the fitness function is defined as the maximum prediction error in all evolutionary algorithms. The coefficients computed by these algorithms compared to coefficients obtained by traditional autocorrelation method in term of prediction accuracy. Our results showed that coefficients obtained by evolutionary algorithms predict the original signal with less prediction error than autocorrelation methods. The maximum prediction error achieved by autocorrelation method, GA, PSO, DE and PSO-DV are 0.35, 0.06, 0.02, 0.07 and 0.001, respectively. This shows that the hybrid algorithm, PSO-DV, is superior to other algorithms in computing linear prediction coefficients.

The aim of this study was to select the best calibration model for determination of propofol plasma concentration by high-performance liquid chromatography method. Determination of propofol in plasma after deproteinization with acetonitrile containing thymol (as internal standard) was carried out on a C18 column with a mixture of acetonitrile and trifluoroacetic acid 0.1% (60:40) as mobile phase which delivered at the flow rate of 1.2 mL/minute. Fluorescence detection was done at the excitation and emission wavelengths of 276 and 310 nm, respectively. After fitting different equations to the calibration data using weighted regression, the adequacy of models were assessed by lack-of-fit test, significance of all model parameters, adjusted coefficient of determination (R2adjusted) and by measuring the predictive performance with median relative prediction error and median absolute relative prediction error of the validation data set. The best model was a linear equation without intercept with median relative prediction error and median absolute relative prediction error of 4.0 and 9.4%, respectively in the range of 10-5000 ng/mL. The method showed good accuracy and precision. The presented statistical framework could be used to choose the best model for heteroscedastic calibration data for analytes like propofol with wide range of expected concentration.

This paper examines the effectiveness of cash distributions to shareholders on operational and free cash flow predictability.
The Wilcoxon rank sum test has been used to compare the prediction errors and data from 123 firms listed in Tehran Stock Exchange (TSE) for the period from 2002 to 2011 has been examined.
Results show that operational and free cash flow prediction errors are different among firms with least and most net cash distributions to shareholders, and also with least and most size. Also, results indicate that, after controlling firm size effect, net cash distributions to shareholders have effect on operational cash flow prediction error, however, after controlling firm size effect, net cash distributions to shareholders have no effect on free cash flow prediction error.
Actually this study suggests that net cash distribution to shareholders is a sign of future condition of operational cash flow.

Survival models are statistical technique to estimate or predict the overall time up to specific events. Prediction is important in medical science and the accuracy of prediction is determined by a measurement, generally based on loss functions, called prediction error. The aim of this study is using parametric models to determine the factors influencing predicted survival time for patients with colorectal cancer (CRC) and select the best model by predicting error’s technique. 600 colorectal cancer patients whom admitted to the Cancer Registry Center of Gastroenterology and Liver Disease Research Center, Taleghani Hospital, Tehran, who were followed at least for 5 years and have completed information selected for this study. Body Mass Index (BMI), Sex, family history of CRC, tumor site, stage of disease and histology of tumor included in the analysis. The survival time was compared by the Log-rank test and multivariate analysis was carried out using parametric. For selecting the best model, the prediction error by apparent loss was used. Log rank test showed better survival for females, BMI more than 25, patients with early stage at diagnosis and patients with colon tumor site. Prediction error by apparent loss was estimated indicated that Weibull model was the best one for multivariate analysis. BMI and Stage were independent prognostic factors according to Weibull model. In this study, Weibull regression showed a better fit according to prediction error. Prediction error would be a criterion to select the best model with ability to make prediction of prognostic factors in survival analysis.

Recently, Rayleigh distribution has received considerable attention in the statistical literature. This paper describes the Bayesian prediction of the ýone parameter Rayleigh ýýdistribution when the data are Type II censored data. Suppose we are planning to collect a Type II censored sample from the ýone parameter Rayleigh distribution in order to find a point predictor for a future order statistic with smallest mean squared prediction error (MSPE) among other point predictors. Although, considering large values for failure numbers yields a point predictor with smaller MSPE, the average cost may increase considerably. One question arises here that “How many failure is enough?”. The aim of this paper is finding the optimal value for number of failures in Type II censoring by considering two criteria, total cost of experiment and mean squared prediction error. Towards this end, we find the Bayesian point predictor for the parameter of distribution. Then, the optimal value for number of failures is obtained when the mean squared prediction error and the total cost of experiment are bounded. To show the usefulness of the obtained results, a simulation study is presented.

Purpose To compare the performance of OKULIX ray-tracing software with SRK-T and Hoffer Q formula in intraocular lens (IOL) power calculation in patients presenting with cataract.
Methods In this prospective study, 104 eyes of 104 patients with cataract who underwent phacoemulsification and IOL implantation were recruited. Three IOL brands were used and for all eyes, IOL power calculation was performed using SRK-T, Hoffer Q formula and also OKULIX ray-tracing software. For all patients, axial length and keratometry data was obtained with IOLMaster 500 device and IOL power was determined using Hoffer Q and SRK-T formula. The IOL powers were also calculated using the OKULIX ray-tracing software combined with CASIA AS-OCT and IOLMaster 500 device. Optically measured axial length of eyes were inserted to OKULIX software from IOLMaster 500 device, and anterior and posterior tomographic and corneal pachymetry data was imported from CASIA AS-OCT into the OKULIX.
The performance of each calculation methods was measured by subtracting the predicted postoperative refraction from the postoperative manifest refraction spherical equivalent (MRSE). For each of the 3 methods, the mean absolute prediction error was determined, too.
Results The mean value absolute prediction error by OKULIX, SRK-T and Hoffer Q formulas, respectively, were 0.42 (±0.03), 0.36 (±0.02) and 0.37 (±0.02). The mean absolute prediction error by OKULIX had no significant difference between three IOL groups (P = 0.96), and it was confirmed that there was no meaningful statistically difference in mean absolute prediction error between the OKULIX, SRK-T and Hoffer Q formula. (P = 0.25). Also in each group of implanted IOLs, all three formulas worked with the same accuracy. The prediction error using OKULIX were within ±0.50 diopter in 63.5% of eyes and within ±1.00 diopter in 94.2% of eyes.
Conclusion OKULIX ray-tracing IOL power measurements provides reliable and satisfactory postoperative results, which are comparable to other 3rd generation formulas of SRK-T and Hoffer Q.

The current study aims to investigate the relationship between stock liquidity risk and financial information quality criteria (i. e. the timely dividends announcement، accruals quality and the percentage of profitability prediction error) of companies listed on the Tehran Stock Exchange. For this purpose، 148 cases of data from listed companies، collected from 2007 to 2012، were employed in order to test the hypotheses during 2007-2012. The results of the study reveal that there is a significant relationship between liquidity risk (the dependent variable) with quality of accruals، percentage of profitability prediction error and timely dividends announcement (independent variables). High levels of accruals quality and timely dividends announcement، cause reduction in stock''s liquidity risk، and high percentages of profitability prediction error increase the stocks'' liquidity risk.

Colorectal cancer is the most common gastrointestinal cancer. Investigating the factors that predict survival time for these patients is important. The purpose of this study was comparison of parametric models by estimating the prediction error and also identifying the effective factors on predicted survival time of patients with colorectal cancer. This cohort study was conducted with 600 patients who were suffered from colorectal cancer in Taleghani Hospital of Tehran between 2001 to 2005 and they were followed up for at least 5 years. For identifying the effective factors on survival time، of the patients we analyzed the data by some parametric models such as Weibull، Exponential and Log logistic and compared these models with the estimation of prediction error by apparent loss method. Among 600 patients there was 344 men (57. 3%) and 256 wemon (47. 7%). Of total، 151 patients were died that 62. 3% of them were men. Univariate analysis showed that the effect of BMI، sex، staging of tumor، tumor site were significant but in multivariate model staging of tumor and BMI were significant. By the estimation of prediction error، the best model was Log logistic. With respect to the importance of survival time prediction، we found that we can use the prediction error to compare the parametric models. In addition، because of effectiveness of tumor stages and BMI in the patients’ survival time، survival time could be increased by an on-time diagnosis and an appropriate controled diet.

This study has used the estimations of selected firm's equity values to determine the effect of earnings disaggregation in predicting their equity values. In this research three levels of earnings disaggregation; such as aggregate earnings, cash flows and total accruals, and cash flows and four major components of accruals was proposed. To test the research hypothesis, the comparison of means (medians) for prediction absolute error and prediction squared errors was used. The participants of this research which were selected based on some special characteristics, were 112 firms, accepted in stock exchange market of Tehran. The gathered data, after preparation and estimation were put through the Excel program and then its final analysis was done through SPSS program. Based on the obtained estimations in industry, some decreases in prediction errors while disaggregation of earnings to cash flows and total accruals were observed. When earnings were divided to cash flows and four major components of accruals, prediction errors were minimum.

On the basis of theoretical principles of financial reporting, the primary goal of financial reporting is to assist the investors to make economic decisions. Economic decisions are related to optimal allocation of the resources. One of the most important decisions is related to investment in stocks which is itself considered as a function of the value of the investment seeking company. In its conceptual statement of financial accounting No. 1, the Financial Accounting Standard Board (FASB) announced the presentation of useful information to the investors, creditors and other potential and present users for assessing the amounts, timing and uncertainty of cash flow as one of the goals of financial reporting. In spite of the knowledge of FASB about the importance of the amounts contained in financial reports, it has presented little guidance on the manner of using these amounts in its conceptual statement. Therefore, in order to fill out this gap, valuation models based on accounting have provided guidance in this area (Barth et al, 2005: 312). Accounting valuation models by information in financial statements evaluate a company. In the collection of the financial statements, the earnings enjoys special position and within the framework of the accepted accounting principles and in the accrual accounting system it includes two parts of cash flow and accrual. Regarding the ability of the management in the application of different methods of accounting for distortion and adjustment of the earnings through personal drive by accruals and with regard to the existing empirical evidence in this area based on the information content of these items, it is reasoned that accruals can be taken into consideration as an index for the determination of the improvement or the bankruptcy of the company (Palepu, 2000). Therefore, study of the effect of the accruals and its components on the value of the company seems necessary to assist the investors for investment decision making. Research Questions or Hypothesis: In this paper the ability of aggregated accruals and its components in forecasting abnormal earnings and explaining value of companies have been evaluated. The main purpose of this paper is answering the question: "Does disaggregating accruals to its four main components and disaggregating abnormal earnings considering the sign of abnormal earnings improve forecasting abnormal earnings and explaining values of companies?" Four main components of accruals are change in receivables, change in payables, change in inventory and depreciation. For this purpose six hypotheses are compiled. For testing the research hypothesis three linear information valuation models based on the framework of Ohlson Model are used. Then, for each linear information model distribution of prediction errors is constructed. For each distribution, absolute percentage error is calculated and to assess the statistical significance of differences in prediction errors, means for absolute percentage error are compared. To estimate the models, we use financial information of 70 sample companies for the period of 1995 to 2008 and pooled data method. The results indicate that the aggregated accruals have power for decreasing prediction errors of company value and abnormal earnings. Furthermore, four main components of accruals can decrease prediction errors of abnormal earnings but can not decrease prediction errors of company value. These results hold both the general model and the disaggregated sign model. Another result of this paper indicates that aggregating abnormal earnings by its sign does not decrease prediction errors of abnormal earnings and company value significantly. Discussion and In general, it is inferred from the findings of the present research that in spite of the aggregated accruals in the estimation of abnormal earnings and value of company that not significantly improve forecasting abnormal earnings and explaining value of company. These findings are also true for the four components of accruals to explaining value of company. Additionally, the sign of abnormal earnings has no significant effect on the improvement of the prediction of abnormal earnings.