Statistics

1704 Submissions

[11] viXra:1704.0383 [pdf] submitted on 2017-04-28 23:11:55

One Step Forecasting Model {Simple Model} (Version 6)

Authors: Ramesh Chandra Bagadi
Comments: 4 Pages.

In this research investigation, the author has presented two Forecasting Models
Category: Statistics

[10] viXra:1704.0382 [pdf] submitted on 2017-04-28 23:39:19

One Step Forecasting Model {Advanced Model} (Version 3)

Authors: Ramesh Chandra Bagadi
Comments: 6 Pages.

In this research investigation, the author has presented two Forecasting Models.
Category: Statistics

[9] viXra:1704.0371 [pdf] submitted on 2017-04-27 23:10:31

One Step Forecasting Model {Simple Model} (Version 5)

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has presented a Forecasting Model
Category: Statistics

[8] viXra:1704.0370 [pdf] submitted on 2017-04-27 23:45:03

One Step Forecasting Model {Advanced Model} (Version 6)

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has presented an Advanced Forecasting Model.
Category: Statistics

[7] viXra:1704.0368 [pdf] submitted on 2017-04-28 02:54:24

One Step Forecasting Model {Advanced Model} (Version 2)

Authors: Ramesh Chandra Bagadi
Comments: 3 Pages.

In this research investigation, the author has presented an Advanced Forecasting Model.
Category: Statistics

[6] viXra:1704.0344 [pdf] submitted on 2017-04-26 06:16:55

One Step Forecasting Model {Version 4}

Authors: Ramesh Chandra Bagadi
Comments: 4 Pages.

In this research investigation, the author has presented two forecasting models.
Category: Statistics

[5] viXra:1704.0332 [pdf] submitted on 2017-04-24 23:04:13

One Step Forecasting Model {Version 3}

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has presented two one step forecasting models.
Category: Statistics

[4] viXra:1704.0314 [pdf] submitted on 2017-04-24 04:51:58

One Step Forecasting Model (Version 2)

Authors: Ramesh Chandra Bagadi
Comments: 2 Pages.

In this research investigation, the author has presented two Forecasting Models.
Category: Statistics

[3] viXra:1704.0277 [pdf] replaced on 2017-06-02 14:03:25

An Indirect Nonparametric Regression Method for One-Dimensional Continuous Distributions Using Warping Functions

Authors: Zhicheng Chen
Comments: 4 Pages.

Distributions play a very important role in many applications. Inspired by the newly developed warping transformation of distributions, an indirect nonparametric distribution to distribution regression method is proposed in this article for distribution prediction. Additionally, a hybrid approach by fusing the predictions respectively obtained by the proposed method and the conventional method is further developed for reducing risk when the predictor is contaminated.
Category: Statistics

[2] viXra:1704.0246 [pdf] submitted on 2017-04-19 20:55:22

Remark On Variance Bounds

Authors: R. Sharma, R. Bhandari
Comments: 5 Pages.

It is shown that the formula for the variance of combined series yields surprisingly simple proofs of some well known variance bounds.
Category: Statistics

[1] viXra:1704.0063 [pdf] replaced on 2018-08-03 10:13:16

Group Importance Sampling for Particle Filtering and MCMC

Authors: L. Martino, V. Elvira, G. Camps-Valls
Comments: 47 Pages. (to appear) Digital Signal Processing, 2018.

Bayesian methods and their implementations by means of sophisticated Monte Carlo techniques have become very popular in signal processing over the last years. Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples. In this work, we study the assignation of a single weighted sample which compresses the information contained in a population of weighted samples. Part of the theory that we present as Group Importance Sampling (GIS) has been employed implicitly in dierent works in the literature. The provided analysis yields several theoretical and practical consequences. For instance, we discuss theapplication of GIS into the Sequential Importance Resampling framework and show that Independent Multiple Try Metropolis schemes can be interpreted as a standard Metropolis-Hastings algorithm, following the GIS approach. We also introduce two novel Markov Chain Monte Carlo (MCMC) techniques based on GIS. The rst one, named Group Metropolis Sampling method, produces a Markov chain of sets of weighted samples. All these sets are then employed for obtaining a unique global estimator. The second one is the Distributed Particle Metropolis-Hastings technique, where dierent parallel particle lters are jointly used to drive an MCMC algorithm. Dierent resampled trajectories are compared and then tested with a proper acceptance probability. The novel schemes are tested in dierent numerical experiments such as learning the hyperparameters of Gaussian Processes, two localization problems in a wireless sensor network (with synthetic and real data) and the tracking of vegetation parameters given satellite observations, where they are compared with several benchmark Monte Carlo techniques. Three illustrative Matlab demos are also provided.
Category: Statistics