Date of Award

2023-08-01

Degree Name

Master of Science

Department

Statistics

Advisor(s)

Suneel B. Chatla

Abstract

We develop a flexible single-index multinomial model for analyzing crime data. In additionto the number of crimes reported, the data also includes covariates such as location, time of day, weather, and other demographic factors. We provide an estimation algorithm and develop R code for the single-index multinomial model. Using simulations, we evaluate the performance of the proposed estimation algorithm. When applied to crime data, the single-index multinomial model provides important insights into crime trends and risk variables, assisting in the development of tailored crime prevention programs. Policymakers and law enforcement organizations can use the model's projections to more efficiently allocate resources and design preemptive strategies to solve crime-related concerns. Finally, the single-index multinomial model demonstrates itself to be a reliable tool for assessing crime data and improving knowledge and management of crime occurrences in varied areas. Keywords: Single-index model, High-dimensional data, Generalized additive model, Crime data

Language

en

Provenance

Recieved from ProQuest

File Size

45 p.

File Format

application/pdf

Rights Holder

Kwabena Gyamfi Duodu

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