Function estimation/approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest-descent minimization. A general gradient descent “boosting” paradigm is developed for additive expansions based on any fitting criterion.Specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification. Special enhancements are derived for the particular case where the individual additive components are regression trees, and tools for interpreting such “TreeBoost” models are presented. Gradient boosting of regression trees produces competitive, highly robust, interpretable procedures for both regression and classification, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire and Friedman, Hastie and Tibshirani are discussed.

/pdf/greedy-function-approximation-a-gradient-boosting-machine-13vts2e34v.pdf

Greedy function approximation: A gradient boosting machine.

/pdf/modern-applied-statistics-with-s-fourth-edition-fw5louzblu.pdf

Modern Applied Statistics with S Fourth edition

The development of specialized software for spatial data analysis has seen rapid growth since the lack of such tools was lamented in the late 1980s by Haining (1989) and cited as a major impediment to the adoption and use of spatial statistics by GIS researchers. Initially, attention tended to focus on conceptual issues, such as how to integrate spatial statistical methods and a GIS environment (loosely vs. tightly coupled, embedded vs. modular, etc.), and which techniques would be most fruitfully included in such a framework. Familiar reviews of these issues are represented in, among others, Anselin and Getis (1992), Goodchild et al. (1992), Fischer and Nijkamp (1993), Fotheringham and Rogerson (1993, 1994), Fischer et al. (1996), and Fischer and Getis (1997). Today, the situation is quite different, and a fairly substantial collection of spatial data analysis software is readily available, ranging from niche programs, customized scripts and extensions for commercial statistical and GIS packages, to a burgeoning open source effort using software environments such as R, Java and Python. This is exemplified by the growing contents of the software tools clearing house maintained by the U.S.- based Center for Spatially Integrated Social Science [CSISS] (see http://www.csiss.org/clearinghouse/).

https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.0016-7363.2005.00671.x

GeoDa: An Introduction to Spatial Data Analysis

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.

/pdf/information-visualization-and-visual-data-mining-2njv2xnkk8.pdf

Information visualization and visual data mining

The counterfactual decomposition technique popularized by Blinder (1973, Journal of Human Resources, 436–455) and Oaxaca (1973, International Economic Review, 693–709) is widely used to study mean outcome differences between groups. For example, the technique is often used to analyze wage gaps by sex or race. This article summarizes the technique and addresses several complications, such as the identification of effects of categorical predictors in the detailed decomposition or the estimation of standard errors. A new command called oaxaca is introduced, and examples illustrating its usage are given.

https://journals.sagepub.com/doi/pdf/10.1177/1536867X0800800401

The Blinder–Oaxaca decomposition for linear regression models

A data management system for storing data in a multiple-level cache arrangement of a database comprises a multi-tier cache memory for initially storing all data in summary form in a secondary cache which may be the database; a processor for receiving requests for data and for moving requested data from the secondary cache to a primary cache, wherein, when subsequent requests for data are received, the primary cache is searched before the secondary cache; and for periodically synchronizing and merging all data in the primary cache back into said secondary cache to refresh said primary cache and remove stale information. The system is particularly useful for managing a telecommunications system call detail summary database in which telephone call details are collected as AMA records after the calls terminate and the AMA records are forwarded to a call detail database for storage in summary form and analysis by an external system, for example, for fraud analysis or billing purposes.

Database management system with a multiple-level cache arrangement

Cave plots are a graphical techniques for comparing two time series using overlapping and opposing coordinate systems. The plots are space-efficient, allowing comparisons involving many data points and can be used with irregularly-spaced time series.

Method of displaying multiple time series and display generated thereby

The map() function in S permits a wide range of line and filled area maps to be drawn for use with map-oriented data analysis. The data underlying these maps is stored in a geographical database. This report describes the format of the geographical database and the steps involved in constructing one. The process is illustrated by our experience in creating a database for the coastlines and national political boundaries of the world. The world database allows data to be displayed on a map of the entire world or on smaller subsets such as Europe or individual countries. The data comes from the World Data Bank II (US Dept. of Commerce) but we have performed edits to correct digitizing errors and have added accurate descriptions of the polygonal regions that make up each country. Because the database contains polygonal information, choropleth maps can be drawn that use data values to control the color of regions, and because it is so detailed, maps can be made in a wide range of scales.

/pdf/constructing-a-geographical-database-30c2ibss9h.pdf

Constructing a Geographical Database

The present invention relates to fast data retrieval. The present invention discloses storing call detail data into two databases. A portion of the total call detail data available is mapped into an interpreted usage event (IUE) and stored in a first database that is indexed for quick data retrieval utilizing a standard database management system. The total raw call detail data is stored in a second database that is less structured, with respect to the first database, without requiring explicit indices. IUEs are retrieved from the first database in response to queries specifying one or more of the characteristics of the desired IUEs. Call detail data stored in the second database is retrieved in response to queries specifying the characteristics from one or more of the retrieved IUEs.

Method for mapping call detail data into a database for quick retrieval

Statistical software, graphics and future workstations for data analysis

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Richard Alan Becker

Author Tools

Chat about Author