Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- And End Results (SEER) (1)
- Autoregressive (1)
- Bi-directional tests (1)
- Change point identification (1)
- Columbus Ohio data (1)
-
- Cox Proportional Hazards Model (1)
- Empirical recurrence rates and ratios (1)
- Epidemiology (1)
- Gradient descent (1)
- Hispanic (1)
- Information theory (1)
- Lattice structure (1)
- Ord's eigenvalue (1)
- Point processes (1)
- Population Based Cohort Study (1)
- Prostate Cancer (1)
- Repairable systems (1)
- Spatial data (1)
- Spatial parameter estimation (1)
- Statistical inference (1)
- Step intensities (1)
- Stochastic processes (1)
- Surveillance (1)
- Survival Analysis (1)
Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Estimation Of The Parameters In A Spatial Regressive-Autoregressive Model Using Ord's Eigenvalue Method, Sajib Mahmud Mahmud Tonmoy
Estimation Of The Parameters In A Spatial Regressive-Autoregressive Model Using Ord's Eigenvalue Method, Sajib Mahmud Mahmud Tonmoy
UNLV Theses, Dissertations, Professional Papers, and Capstones
In this thesis, we study one of Ord's (1975) global spatial regression models.
Ord considered spatial regressive-autoregressive models to describe the interaction
between location and a response variable in the presence of several covariates. He also
developed a practical estimation method for the parameters of this regression model
using the eigenvalues of a weight matrix that captures the contiguity of locations.
We review the theoretical aspects of his estimation method and implement it in the
statistical package R.
We also implement Ord's methods on the Columbus, Ohio, crime data set from the
year 1980, which involves the crime rate of …
Bi-Directional Testing For Change Point Detection In Poisson Processes, Moinak Bhaduri
Bi-Directional Testing For Change Point Detection In Poisson Processes, Moinak Bhaduri
UNLV Theses, Dissertations, Professional Papers, and Capstones
Point processes often serve as a natural language to chronicle an event's temporal evolution, and significant changes in the flow, synonymous with non-stationarity, are usually triggered by assignable and frequently preventable causes, often heralding devastating ramifications. Examples include amplified restlessness of a volcano, increased frequencies of airplane crashes, hurricanes, mining mishaps, among others. Guessing these time points of changes, therefore, merits utmost care. Switching the way time traditionally propagates, we posit a new genre of bidirectional tests which, despite a frugal construct, prove to be exceedingly efficient in culling out non-stationarity under a wide spectrum of environments. A journey surveying …
Prostate Cancer Survival Among Hispanics: A Surveillance, Epidemiology, And End Results (Seer) Population-Based Cohort Study, David Rivas
UNLV Theses, Dissertations, Professional Papers, and Capstones
Hispanics are now the youngest, largest, and fastest growing minority group in the U.S. Prostate cancer (PC) is the most commonly diagnosed cancer in men and is the second-leading cause of cancer deaths among Hispanics. For the first time, we examined PC-specific survival among distinct Hispanic groups that include Mexicans, Cubans, Dominicans, Puerto Ricans, as well as Central and South Americans. We compared these groups to the main reference population in the U.S., non-Hispanic Whites (NHW), after adjustment for prognostic factor risk categories (prostate-specific antigen (PSA) level, Gleason score, and tumor stage), as well as sociodemographic covariates (e.g., health insurance, …
Fundamental Tradeoffs In Estimation Of Finite-State Hidden Markov Models, Justin Le
Fundamental Tradeoffs In Estimation Of Finite-State Hidden Markov Models, Justin Le
UNLV Theses, Dissertations, Professional Papers, and Capstones
Hidden Markov models (HMMs) constitute a broad and flexible class of statistical models that are widely used in studying processes that evolve over time and are only observable through the collection of noisy data. Two problems are essential to the use of HMMs: state estimation and parameter estimation. In state estimation, an algorithm estimates the sequence of states of the process that most likely generated a certain sequence of observations in the data. In parameter estimation, an algorithm computes the probability distributions that govern the time-evolution of states and the sampling of data. Although algorithms for the two problems are …