Mean square error

In this work, we propose carrying out tests on the encoding sequence of a video sequence to improve data flow and average PSNR. We experiment with the choice of reference images, in the process of video compression, by using only the intra and predicted images extracted from sequences. For each intra and predicted image, we perform edge detection. Each image of the sequence is compared with the other images by subtracting corresponding edges. The choice of the reference image is based on the result of subtraction. We adopt the criterion of minimum pixels if the resulting images present only points and the criterion of minimum distance between the lines if they present parallel lines and possibly points. Testing this approach on News and Kiss cool video sequences revealed an improvement in data flow and average PSNR as compared to the original encoding and choosing reference images based on the mean square error.

We propose a new ratio estimator using two auxiliary variables in simple random sampling. We obtain mean square error (MSE) equation of this estimator and theoretically show that our proposed estimator is more efficient than the traditional multivariate ratio estimator under a defined condition. In addition, we support this theoretical result with the aid of a numerical example.

Most of the statistical procedures in meta-analysis are based on the estimation of average effect sizes from a set of primary studies. The optimal weight for averaging a set of independent effect sizes is the inverse variance of each effect size, but in practice these weights have to be estimated, being affected by sampling error. When assuming a random-effects model, there are two alternative procedures for averaging independent effect sizes: Hunter and Schmidt’s estimator, which consists of weighting by sample size as an approximation to the optimal weights; and Hedges and Vevea’s estimator, which consists of weighting by an estimation of the inverse variance of each effect size. In this article, the bias and mean squared error of the two estimators were assessed via Monte Carlo simulation of meta-analyses with the standardized mean difference as the effect-size index. Hedges and Vevea’s estimator, although slightly biased, achieved the best performance in terms of the mean square...

Abstract.  We propose simple parametric and nonparametric bootstrap methods for estimating the prediction mean square error (PMSE) of state vector predictors that use estimated model parameters. As is well known, substituting the model parameters by their estimates in the theoretical PMSE expression that assumes known parameter values results in underestimation of the true PMSE. The parametric method consists of generating parametrically a large number of bootstrap series from the model fitted to the original series, re-estimating the model parameters for each series using the same method as used for the original series and then estimating the separate components of the PMSE. The nonparametric method generates the series by bootstrapping the standardized innovations estimated for the original series. The bootstrap methods are compared with other methods considered in the literature in a simulation study that also examines the robustness of the various methods to non-normality of the model error terms. Application of the bootstrap method to a model fitted to employment ratios in the USA that contains 18 unknown parameters, estimated by a three-step procedure yields unbiased PMSE estimators.

In response to the growing concern over the use of fossil fuels, renewable energy industries have been significant economic drivers in many parts of the United States. In the recent years there is a strong growth in solar power generation industries that requires prediction of solar energy to develop highly efficient stand-alone photovoltaic systems as well as hybrid power systems. In order to accomplish the goal, we propose a predictive model that is based on recurrent neural networks trained with the Levenberg-Marquardt backpropagation learning algorithm to forecast the solar radiation using the past solar radiation and solar energy. This computational intelligence modeling tool explored the impact of solar radiation and solar energy in forecasting reliable long-run solar energy. Based on the excellent experimental results including the mean squared error analysis, error autocorrelation function analysis, regression analysis, and time series response, it demonstrated that the prop...

Forecasting stock price or stock index is an important financial subject that has attracted researchers' attention for many years. In this paper, we put forward a new method called HLP as data preprocessing to process the stock data. By HLP method we can get the stock high low point with different frequency and amplitude. The extracted data describes the feature of stock price movement. After that we construct ANN models to forecast the stock movement direction and price. The HLP method and ANN models give assistance to investors.

In the present paper, we have proposed improved predictive approach for estimation of population mean. The proposed difference type approach works better in comparison with existing works by various authors. A comprehensive comparative study is carried out both theoretically and numerically to study the merit of the considered estimator. Keywords: Predictive approach, Bias, Mean square error, Auxiliary information. I. INTRODUCTION Sample survey is a cost effective method of data collection and is used for drawing valid inference about population parameters. The main aim of sample survey is to get an efficient estimate of a population. Hence, in order to enhance the efficiency of estimators of parameters one can use additional information which is correlated with the information under study and about which the data is accessible before the initiation of the survey process known as auxiliary information. The literature portrays a wide array of techniques for using auxiliary information in concern of product, ratio and regression methods for population mean estimation which most of the time leads to gain in terms of efficiency of the estimator. Basu (1971) encountered a prediction approach to estimate population mean by predicting or guessing the mean of unobserved units and combined the aforementioned mean with mean of sampled units of understudy population. Based on this approach, various decision-theorists might unwilling to make estimator choice. But to represent "heart of the matter" Basu (1971) adopted this prediction approach for estimating population mean [see Cassel et al. (1977, p.110)]. Further, Srivastava (1983) extended this approach by proposing a product estimator under predictive estimation approach for estimating finite population mean. Singh et al. (2014) developed the exponential based ratio and product type predictive estimators for finite population mean using auxiliary information. On the similar basis Yadav, Mishra and Kumar (2014) proposed improved exponential ratio and product based predictive estimators for prediction of finite population mean then after Yadav and Mishra (2015) developed an improved ratio cum product type predictive estimator for finite population mean using auxiliary information. Motivated with the work based on prediction approach, we here proposed a Searls(1964) based regression type estimator as a predictor for the mean of the unobserved units of the population and is shown efficient of all previous estimators used to estimate the mean of a finite population through, an empirical study given in the last section of the paper, as the percentage relative efficiency of the proposed estimator with respect to usual estimator is maximum than all other estimators.

In this paper, we examine the daily water demand forecasting performance of double seasonal univariate time series models (Exponential Smoothing, ARIMA and GARCH) based on multi-step ahead forecast mean squared errors. We investigate whether combining forecasts from different methods and from different origins and horizons could improve forecast accuracy. We use daily data for water consumption in Spain from 1 January 2001 to 30 June 2006.