Yet Another Blog in Statistical Computing

Statcompute.wordpress.com

September 1, 2021 03:47:16 AM

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<H1>	<H2>	<H3>	<H4>	<H5>	<H6>
155	3	1	0	0	0

<H1> Yet Another Blog in Statistical Computing </H1>
<H1> Categories </H1>
<H1> Top Posts & Pages </H1>
<H1> R </H1>
<H1> YAP: Yet Another Probabilistic Neural Network </H1>
<H1> Improve General Regression Neural Network by Monotonic Binning </H1>
<H1> GRNN with Small Samples </H1>
<H1> GRNN vs. GAM </H1>
<H1> Permutation Feature Importance (PFI) of GRNN </H1>
<H1> Partial Dependence Plot (PDP) of GRNN </H1>
<H1> Merge MLP And CNN in Keras </H1>
<H1> Assess Variable Importance In GRNN </H1>
<H1> Hyper-Parameter Optimization of General Regression Neural Networks </H1>
<H1> Modeling Practices of Operational Losses in CCAR </H1>
<H1> Develop Performance Benchmark with GRNN </H1>
<H1> Dummy Is As Dummy Does </H1>
<H1> Improve GRNN Efficiency by Weighting </H1>
<H1> Yet Another R Package for General Regression Neural Network </H1>
<H1> Monotonic Binning Driven by Decision Tree </H1>
<H1> Chunk Averaging of GLM </H1>
<H1> Latin Hypercube Sampling in Hyper-Parameter Optimization </H1>
<H1> Parallel R: Socket or Fork </H1>
<H1> WoE Transformation for Loss Given Default Models </H1>
<H1> Faster Way to Slice Dataframe by Row </H1>
<H1> Granular Weighted Binning by Generalized Boosted Model </H1>
<H1> Why Use Weight of Evidence? </H1>
<H1> More General Weighted Binning </H1>
<H1> Binning with Weights </H1>
<H1> Batch Deployment of WoE Transformations </H1>
<H1> Batch Processing of Monotonic Binning </H1>
<H1> Monotonic Binning with GBM </H1>
<H1> Deployment of Binning Outcomes in Production </H1>
<H1> A Summary of My Home-Brew Binning Algorithms for Scorecard Development </H1>
<H1> Bayesian Optimization for Hyper-Parameter </H1>
<H1> Gradient-Free Optimization for GLMNET Parameters </H1>
<H1> Direct Optimization of Hyper-Parameter </H1>
<H1> Sobol Sequence vs. Uniform Random in Hyper-Parameter Optimization </H1>
<H1> Co-integration and Mean Reverting Portfolio </H1>
<H1> Statistical Assessments of AUC </H1>
<H1> Phillips-Ouliaris Test For Cointegration </H1>
<H1> An Utility Function For Monotonic Binning </H1>
<H1> Improving Binning by Bootstrap Bumping </H1>
<H1> More Robust Monotonic Binning Based on Isotonic Regression </H1>
<H1> Creating List with Iterator </H1>
<H1> XFrames: Another Convenient Python Interface to Spark </H1>
<H1> Growing List vs Growing Queue </H1>
<H1> Convert Data Frame to Dictionary List in R </H1>
<H1> Fetching Data From SAS Dataset to Lua Table </H1>
<H1> Data Wrangling with Astropy Package </H1>
<H1> Manipulating Dictionary List with SQLite Back-End </H1>
<H1> Joining Dictionary Lists by Key </H1>
<H1> Aggregation of Dictionary List by Key </H1>
<H1> Convert SAS Dataset to Dictionary List </H1>
<H1> Grouping Dictionary List by Key </H1>
<H1> Subset Dictionary by Values </H1>
<H1> Subset Dictionary by Keys </H1>
<H1> Import CSV as Dictionary List </H1>
<H1> Monotonic Binning with Equal-Sized Bads for Scorecard Development </H1>
<H1> Two-Stage Estimation of Switching Regression </H1>
<H1> By-Group Summary with SparkR – Follow-up for A Reader Comment </H1>
<H1> Union Multiple Data.Frames with Different Column names </H1>
<H1> Why Vectorize? </H1>
<H1> How to Avoid For Loop in R </H1>
<H1> Modeling Frequency Outcomes with Ordinal Models </H1>
<H1> Playing Map() and Reduce() in R – Subsetting </H1>
<H1> Playing Map() and Reduce() in R – By-Group Calculation </H1>
<H1> Writing Wrapper in SAS </H1>
<H1> More Flexible Ordinal Outcome Models </H1>
<H1> Adjacent-Categories and Continuation-Ratio Logit Models for Ordinal Outcomes </H1>
<H1> Ordered Probit Model and Price Movements of High-Frequency Trades </H1>
<H1> Co-integration and Pairs Trading </H1>
<H1> Subset by Index in Clojure </H1>
<H1> SAS Implementation of ZAGA Models </H1>
<H1> Mimicking SQLDF with MonetDBLite </H1>
<H1> MLE with General Optimization Functions in R </H1>
<H1> Read Random Rows from A Huge CSV File </H1>
<H1> Updating Column Values in Clojure Map </H1>
<H1> Adding New Columns to Clojure Map </H1>
<H1> LogRatio Regression – A Simple Way to Model Compositional Data </H1>
<H1> Transpose in Clojure </H1>
<H1> Clojure Integration with R </H1>
<H1> Aggregation by Multiple Keys in Clojure </H1>
<H1> Inner and Outer Joins in Clojure </H1>
<H1> By-Group Statistical Summary in Clojure </H1>
<H1> Subset by Values in Clojure </H1>
<H1> Do We Really Need Dataframe in Clojure? </H1>
<H1> Parse CSV File with Headers in Clojure </H1>
<H1> For Loop and Map in Clojure </H1>
<H1> Clojure and SQLite </H1>
<H1> MLE in R </H1>
<H1> Modeling Dollar Amounts in Regression Setting </H1>
<H1> R Interfaces to Python Keras Package </H1>
<H1> Additional Thoughts on Estimating LGD with Proportional Odds Model </H1>
<H1> Estimating Parameters of A Hyper-Poisson Distribution in SAS </H1>
<H1> Modeling LGD with Proportional Odds Model </H1>
<H1> Query CSV Data with Apache Drill </H1>
<H1> Monotonic WoE Binning for LGD Models </H1>
<H1> Granular Monotonic Binning in SAS </H1>
<H1> Model Non-Negative Numeric Outcomes with Zeros </H1>
<H1> Variable Selection with Elastic Net </H1>
<H1> DART: Dropout Regularization in Boosting Ensembles </H1>
<H1> Model Operational Losses with Copula Regression </H1>
<H1> Sparkling Water and Moving Data Around </H1>
<H1> Model Operational Loss Directly with Tweedie GLM </H1>
<H1> GLM with H2O in R </H1>
<H1> H2O Benchmark for CSV Import </H1>
<H1> Using Tweedie Parameter to Identify Distributions </H1>
<H1> Finer Monotonic Binning Based on Isotonic Regression </H1>
<H1> Joining Tables in SparkR </H1>
<H1> R Interface to Spark </H1>
<H1> Data Aggregation with PySpark </H1>
<H1> Kick Off Spark </H1>
<H1> Double Poisson Regression in SAS </H1>
<H1> SAS Macro Calculating Goodness-of-Fit Statistics for Quantile Regression </H1>
<H1> Random Search for Optimal Parameters </H1>
<H1> A Simple Convolutional Neural Network for The Binary Outcome </H1>
<H1> Modeling Generalized Poisson Regression in SAS </H1>
<H1> Monotonic Binning with Smbinning Package </H1>
<H1> Autoencoder for Dimensionality Reduction </H1>
<H1> An Example of Merge Layer in Keras </H1>
<H1> Dropout Regularization in Deep Neural Networks </H1>
<H1> Estimate Regression with (Type-I) Pareto Response </H1>
<H1> Pregibon Test for Goodness of Link in SAS </H1>
<H1> More about Flexible Frequency Models </H1>
<H1> Modified Park Test in SAS </H1>
<H1> Parameter Estimation of Pareto Type II Distribution with NLMIXED in SAS </H1>
<H1> Fastest Way to Add New Variables to A Large Data.Frame </H1>
<H1> Flavors of SQL on Pandas DataFrame </H1>
<H1> Test Drive Proc Lua – Convert SAS Table to 2-Dimension Lua Table </H1>
<H1> Copas Test for Overfitting in SAS </H1>
<H1> SAS Macro Calculating Mutual Information </H1>
<H1> Scorecard Development with Data from Multiple Sources </H1>
<H1> Risk Models with Generalized PLS </H1>
<H1> Duplicate Breusch-Godfrey Test Logic in SAS Autoreg Procedure </H1>
<H1> More Flexible Approaches to Model Frequency </H1>
<H1> Calculating ACF with Data Step Only </H1>
<H1> Estimate Quasi-Binomial Model with GENMOD Procedure in SAS </H1>
<H1> A More Flexible Ljung-Box Test in SAS </H1>
<H1> SAS Macro Performing Breusch–Godfrey Test for Serial Correlation </H1>
<H1> Python Prototype of Grid Search for SVM Parameters </H1>
<H1> Improve SVM Tuning through Parallelism </H1>
<H1> SAS Macro Calculating LOO Predictions for GLM </H1>
<H1> Where Bagging Might Work Better Than Boosting </H1>
<H1> The Power of Decision Stumps </H1>
<H1> Parallelize Map() </H1>
<H1> Parallelism with Joblib Package in Python </H1>
<H1> Import CSV by Chunk Simultaneously with IPython Parallel </H1>
<H1> Prediction Intervals for Poisson Regression </H1>
<H1> Calculate Leave-One-Out Prediction for GLM </H1>
<H1> Multivariate Adaptive Regression Splines with Python </H1>
<H1> Download Federal Reserve Economic Data (FRED) with Python </H1>
<H1> Fitting Generalized Regression Neural Network with Python </H1>
<H1> Modeling Frequency in Operational Losses with Python </H1>
<H1> Modeling Severity in Operational Losses with Python </H1>
<H1> Posts navigation </H1>
<H2> </H2>
<H2> </H2>
<H2> </H2>
<H3> </H3>

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Yet Another Blog in Statistical Computing

statcompute.wordpress.com/

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Keywords	Freq	Title	Desc	<H>
model	158
data	153
function	143
shown	115
more	85
binning	83
regression	76
based	72
also	67
might	63
would	62
first	58
used	57
each	56
there	52

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URL

http://statcompute.wordpress.com
Length: 11 characters

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PageSpeed Insights (Desktop)

48 / 100

Page Speed

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Typo Availability

Domains (TLD)	Status
qtatcompute.com	Available
wtatcompute.com	Available
etatcompute.com	Available
ztatcompute.com	Available
xtatcompute.com	Available

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PageSpeed Insights (Mobile)

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Technologies

Server IP

Server IP	Server Location	Service Provider
192.0.78.13	United States	Automattic

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Visitors

Estimated Worth

$372 USD

Just a estimated worth of your website based on Alexa Rank.

Traffic Rank

5,637,836th most visited website in the World.

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Your Alexa Rank is a good estimate of the worldwide traffic to your website, although it is not 100 percent accurate.

Visitors Localization

Your website is popular on following countries:

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Link Analysis

In-Page Links

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Anchor	Type	Follow

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Broken Links

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Statcompute.wordpress.com

Search Engine Optimization

Title Tag

Meta Description

Meta Keywords

Headings

Google Preview

Alt Attribute

Keywords Cloud

Keyword Consistency

Text/HTML Ratio

GZIP compression

WWW Resolve

IP Canonicalization

XML Sitemap

Robots.txt

URL Rewrite

Underscores in the URLs

Embedded Objects

Iframe

Domain Registration

WHOIS Data

Indexed Pages

Backlinks Counter

Usability

URL

Favicon

Custom 404 Page

Page Size

Load Time

PageSpeed Insights (Desktop)

48 / 100

Page Speed

Language

Domain Availability

Typo Availability

Email Privacy

Safe Browsing

Mobile

Mobile Friendliness

Mobile View

Mobile Compatibility

PageSpeed Insights (Mobile)

0 / 100

Page Speed

Technologies

Server IP

Speed Tips

Analytics

W3C Validity

Doc Type

Encoding

Social

Social Data

Visitors

Estimated Worth

Traffic Rank

Visitors Localization

Link Analysis

In-Page Links

Broken Links

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