Computational Statistics and Data Analysis Assignment and Project help

Computational Statistics, or statistical computing, is the interface between statistics and computer science. It is the area of computational science specific to the mathematical science of statistics. This area is also developing rapidly, leading to calls that a broader concept of computing should be taught as part of general statistical education.  We have fundamental focus to give Computational Statistics assignment help for the students. Our Experts have the capability to write the content on any referencing styles, while delivering all the projects & assignments are accompanied by substantiation documentation. We put every effort to make on perfect Computational Statistics answer for your work. We have not just claimed supremacy but we also hired many instructors for this. Our instructors provide the help with Computational Statistics homework to the weak students. Our experts are available 24×7 to assist you in your quest for an immaculate assignment solution. We ensure you the excellence and top marks in the class.

We have a team who has helped a number of students in the following topics, pursuing education through regular and online universities, institutes or online Tutoring:

  • ANOVA designs and regression approach
  • Approximation of Functions and Numerical Quadrature
  • Arealistic numerical modeling exercise
  • Categorical data analysis: One-way, two-way and multi-way tables, Effect size coefficients
  • Constrained and non-smooth optimization: convex functions; interior point methods
  • Construction of tests
  • Continuity and differentiability in several variables
  • Continuous functions and Taylor expansions
  • Continuous functions of one real variable
  • Cross validation, regularization, and basic functions
  • Data Randomization, Partitioning, and Augmentation
  • Database Management & Information Retrieval
  • Deterministic approximations for posteriors: variational Bayes, expectation propagation
  • Distributions of linear and quadratic forms
  • Emphasis is placed on regarding the discretized equations as discrete models of the system
  • Estimation of Probability Density Functions Using Parametric Models
  • Exploiting special structure to solve Newton linear equations
  • Exploratory data analysis (EDA): descriptive and Graphical tools for- Univariate data, Bivariate data and Multivariate data
  • Filter-forward-sample-backward algorithm for HMMs
  • Focus on the specific problems encountered in each application area
  • Gibbs sampling: slice sampling, Bayesian lasso, spike-and-slab, hit and run
  • Graphical Methods in Computational Statistics
  • Integration wrap-up: Gaussian quadrature, RJMCMC, bridge sampling, annealed importance sampling
  • Linear, quadratic, and semidefinite programs; support vector methods
  • Maxima and minima of functions
  • Mean value theorem
  • Methods of Computational Statistics
  • Metropolis-Hastings
  • Mixture distributions and Markov Monte Carlo
  • Modern computationally-intensive statistical methods
  • Monte Carlo Methods for Statistical Inference
  • Multiple correlation coefficients
  • Multivariate normal distribution and its properties
  • Non-conservative versus conservative systems
  • Nonparametric Bayes methods
  • Non-parametric density estimation
  • Nonparametric Estimation of Probability Density Functions
  • Nonparametric probability density estimation
  • Numerical solution of partial differential equations
  • Open/closed sets sequences and series
  • Optimization and maximum likelihood estimation
  • Probability Theory and Stochastic Processes
  • Proximal methods, dual decomposition, and convex relaxation
  • Random number generation
  • Rao-Blackwellization. Adaptive simulated tempering.
  • Real numbers, functions, sequences, limits, liminf, limsup, series
  • Robust nonparametric linear regression
  • Simulation from multivariate normal distribution
  • Solution of Nonlinear Equations and Optimization
  • Stability, consistency and convergence
  • Statistical Models of Dependencies
  • Stochastic approximation methods
  • Supervised statistical learning 0including discrimination methods
  • Taylor series expansion
  • Taylor's theorem
  • Term-by-term differentiation and integration
  • Tests for partial and regression coefficients and their associated confidence regions
  • The Jacobian theorem
  • Theorem of Calculus
  • Theorems of Green and Stokes
  • Theoretical Computer Science
  • Tools for Identification of Structure in Data
  • Union-intersection and likelihood ratio principles
  • univariate and multivariate distributions
  • Weier-strass approximation theorem
  • Wishart distribution