Training Support Vector Machines (SVMs) to predict drugs concentrations is often difficult because of the high level of noise in the training data, due to various kinds of measurement errors. We apply RANdom SAmple Consensus (RANSAC) algorithm in this paper to solve this problem, enhancing the prediction accuracy by more than 40% in our particular case study. A personalized sample selection met...

A new linear predictive method is presented in this study. The method, Linear Prediction with Linear Extrapolation (LPLE), reformulates the computation of linear prediction by combining the preceding values of sample x(n) into consecutive sample pairs (i.e., x(n-2i), x(n-2i+1)). Each of these pairs determines a regression line the value of which at time instant n is used as a data sample in the...

MOTIVATION High-dimensional data such as microarrays have created new challenges to traditional statistical methods. One such example is on class prediction with high-dimension, low-sample size data. Due to the small sample size, the sample mean estimates are usually unreliable. As a consequence, the performance of the class prediction methods using the sample mean may also be unsatisfactory. T...

Typically the true error of ANN prediction model is estimated by testing the trained network on new data not used in model construction. Four well-studied statistical error estimation methods: cross-validation, group cross-validation, jackknife and bootstrap are reviewed and are presented as competing error estimation methodologies that could be used to evaluate and validate ANN prediction mode...

As a simple and compelling approach for estimating outof-sample prediction error, cross-validation naturally lends itself to the task of model comparison. However, even with moderate sample size, it can be surprisingly difficult to compare multilevel models based on predictive accuracy. Using a hierarchical model fit to large survey data with a battery of questions, we demonstrate that even tho...

In spatial statistic, the data analyzed which is correlated and this correlation is due to their locations in the studied region. Such correlation that is related to distance between observations is called spatial correlation. Usually in spatial data analysis, the prediction of the amount of uncertain quantity in arbitrary 4locations of the area is considered according to attained observations ...

In this paper, a one-sample point predictor of the random variable X is studied. X is the occurrence of an event in any successive visits $L_i$ and $R_i$ :i=1,2…,n (interval censoring). Our proposed method is based on finding the expected value of the conditional distribution of X given $L_i$ and $R_i$ (i=1,2…,n). To make the desired prediction, our approach is on the basis of approximating the...

‎Lindley distribution has received considerable attention in the statistical literature due to its simplicity‎. ‎In this paper‎, ‎we consider the problem of predicting the failure times of experimental units that are censored in a right-censored sample‎‎ when the underlying lifetime is Lindley distributed‎. ‎The maximum likelihood predictor‎, ‎the Bes...

In 2008, financial tsunami started to impair the economic development of many countries, including Taiwan. The prediction of financial crisis turns to be much more important and doubtlessly holds public attention when the world economy goes to depression. This study examined the predictive ability of the four most commonly used financial distress prediction models and thus constructed reliable ...

Multi-parameter models in systems biology are typically 'sloppy': some parameters or combinations of parameters may be hard to estimate from data, whereas others are not. One might expect that parameter uncertainty automatically leads to uncertain predictions, but this is not the case. We illustrate this by showing that the prediction uncertainty of each of six sloppy models varies enormously a...