Nonparametric smoothing under shape constraints has recently received much well-deserved attention. Powerful methods have been proposed for imposing a single shape constraint such as monotonicity and concavity on univariate functions. In this paper, we extend the monotone kernel regression method in Hall and Huang (2001) to the multivariate and multi-constraint setting. We impose equality and/o...

This research aims to create the most efficient and accurate cab fare prediction system using two machine learning algorithms, Multiple linear Regression algorithm random forest algorithm, compare parameters r-square, Mean Square Error (MSE), Root MSE, RMSLE values evaluate efficiency of algorithm. Considering as group 1 algorithms implemented, 2 process was predict prices get best accuracy alg...

The knowledge of weather is useful for finding climate change over a period. In this present frame work uses 15 years of weather of Hyderabad city , data a real time the datasets collected from weather station. Weather data is a time series and multidimensional data. Outliers are the objects whose behavior is different from the rest. Outliers in weather data represent the cyclone, drought, seas...

We introduce variational Bayes methods for fast approximate inference in functional regression analysis. Both the standard cross-sectional and the increasingly common longitudinal settings are treated. The methodology allows Bayesian functional regression analyses to be conducted without the computational overhead of Monte Carlo methods. Confidence intervals of the model parameters are obtained...