Methods of Statistical Computing Assignment Homework Help

The nature of statistics is changing significantly with many opportunities to broaden the discipline and its impact on science and policy. While there are opportunities for significant change in many dimensions, we focus more narrowly on computing and call for computing concepts to be integrated into the statistics curricula at all levels. Computational literacy and programming are as fundamental to statistical practice and research as mathematics. We advocate that our field needs to define statistical computing more broadly to include advancements in modern computing, beyond traditional numerical algorithms.

Students studying Statistical Computing can avail our help in completing their projects or assignments at a reasonable & minimal cost with quality par excellence in the following topics:

  • Computational methods for statistical computing problems
  • Conjugacy and noninformative priors
  • Correlation, and cross-tabulation
  • Density estimation: kernel density estimators
  • Derivatives and their manipulation
  • Filtering/smoothing and parameter estimation
  • General state-space markov chain theory
  • Gibbs sampler and winbugs
  • Higher-dimensional arrays
  • Importing text and excel files into r
  • Importing text and excel files into sas
  • Least squares estimation problems
  • Lists: single/double linked, Stacks
  • Local inverses and critical points
  • Metropolis-hastings algorithms
  • Missing data: augmented em algorithm
  • Model-based clustering
  • Nonlinear non-gaussian state-space
  • Non-uniform random variable generation
  • Descriptive statistics & graphical analysis
  • Optimization including Lagrange's method
  • Orthogonal Diagonalization and transformations
  • Probability as measure of uncertainty
  • Reduced Row Echelon Form
  • Regression and variable selection
  • Scatter plot smoothing: splines, Gam, Mcmc
  • Solution of nonlinear equations
  • Statistical computing packages
  • Stochastic volatility models
  • Switching state-space models
  • Tangent Hyper planes
  • Time Series Analysis and Forecasting
  • Trees, Graphs, Randomness
  • Variance reduction
  • Variance reduction techniques
  • Vector-valued functions

 
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