The blog posts lists few diagrams, animations and pictorial presentations of the terms you would read frequently while reading Economics focused blog posts, let me know if I can make any technical terms easier to understand. If you are not interested in the subject as such and just seeing the writings, I wouldnâ€™t be writing or explaining any unnecessary terms lined further. You must be having basic understanding of K-12 mathematics and english, my goal is writing minimum text as according to various research visuals are best for understanding anything for that matter. Most of the mathematical functions are represented in terms of F(m).

*Note: You are reading a free typing blog post, I update as and when I do more drawings. Thanks for reading. *

### Monotonic Function

The term is used vigorously while drawing graphs for any study. Monotonic Functions have plots preserving increasing or decreasing sequence.

### Probability Curves

Any graph representing probability across a function F(m).

### Quantile Classification

The quantile classification represents the class and the representation of units in the categorised class.

### Dual Regression

Dual regression is used as an alternative for the quantile regression process for the global estimation of conditional distribution functions under minimal assumptions. Dual regression provide all the interpretational power of the quantile regression process while avoiding for repairing the intersecting conditional quantile surfaces that quantile regression often produces in practice. Our general characterisation is used for specification and estimation of the flexible class of conditional distribution functions, and present asymptotic theory for the corresponding empirical regression process.

### Sieve Wald

Sieve Wald tests under virtually to derive the limiting distributions of the original-sample statistics.

### Lava Attacks

Predictions rely as per having either a sparse signal model, a model in which most parameters are zero and there are a small number of non zero parameters that are large in magnitude, or a dense signal model, a model with a no large parameters and very many small non zero parameters. We consider a generalisation of these two basic models, termed as a â€˜sparse + denseâ€™ model in which the signal is given by the sum of a sparse signal and a dense signal. Lava is computationally efficient suitable choices of penalty parameters the proposed method strictly preferred over lasso and ridge.