Statistical Modeling
Statistical Modeling is the formalization of relationships between variables in the form of mathematical equations.
Model often allows management to pinpoint changes in the environment faster than is possible otherwise.
Models help managers decide what information should be collected. Thus models lead to improved data collection, and their use may avoid the collection and storage of large amounts of data without apparent purpose.
       The whole purpose of statistical modeling is not about the research, it ultimately comes down to providing an insight to solutions. It involves analyzing the data and applying it in different circumstances.

        It has wide range applications  in Marketing, Finance, Healthcare, Pharmaceuticals, Manufacturing, and many more business fields.
Statistical Modeling:

Statistical models are central to applications of statistics and their development motivates new statistical theories and methodologies. Commencing with a review of linear and generalized linear models, analysis of variance and experimental design, the theory of linear mixed models is developed and model selection techniques are introduced. Approaches to non and semiparametric inference, including generalized additive models, are considered. Specific applications may include longitudinal data, survival analysis and time series modelling.

There are several aspects of the model building process, or the process of finding an appropriate learning function. In what proportion data is allocated to certain tasks like model building and evaluating model performance, is an important aspect of modeling.

It is always a good practice to try out alternative models. There is no single model that will always do better than any other model for all data sets.



Our statistical modeling techniques:
Linear regression
Basis function regression
Gaussian process regression
Generalized linear model
Logistic regression
Feed-forward neural network regression
Feed-forward neural network density model
Additive regression
Projection pursuit regression
Exploratory Factor Analysis
Robust regression
Independent Feature Model (`Naive Bayes')
Linear classifier
Generalized linear classifier
Support Vector Machine
Finite mixture model
Markov chain
Autoregression
Hidden Markov Model
Autoregressive Hidden Markov Model
Input/Output Hidden Markov Model
Hidden Markov Decision Tree
Linear Dynamical System
Decision tree
Simultaneous autoregression
Constrained mixture model
Constrained Hidden Markov Model
R
Python
IBM SPSS Modeler
Model selection techniques:
Cross-validation
Bayesian model selection
Minimum Message Length model selection
Nonparametric modeling:
Nearest-neighbor density estimation
Nearest-neighbor classification
Nearest-neighbor regression
Kernel density estimation
Locally weighted regression
Tools Expertise:
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