Figures index

From

Pseudo R2 Probablity Measures, Durbin Watson Diagnostic Statistics and Einstein Summations for Deriving Unbiased Frequentistic Inferences and Geoparameterizing Non-Zero First-Order Lag Autocorvariate Error in Regressed Multi-Drug Resistant Tuberculosis Time Series Estimators

Benjamin G. Jacob, Daniel Mendoza, Mario Ponce, Semiha Caliskan, Ali Moradi, Eduardo Gotuzzo, Daniel A. Griffith, Robert J. Novak

American Journal of Applied Mathematics and Statistics. 2014, 2(5), 252-301 doi:10.12691/ajams-2-5-1
  • Figure 1.1. The 4 vertices in a predictive MDR-TB epidemiological risk model that have been assigned colors while satisfying the constraints at each edge
  • Figure 1.2. A solution to the unique label cover instance for a predictive MDR-TB epidemiological risk model
  • Figure 1.3. An instance of unique label cover that does not allow a satisfying assignment in a MDR-TB risk model
  • Figure 1.4. An assignment that satisfies all edges except the thick edge in a MDR-TB risk model
  • Figure 1. San Juan de Lurancho study site
  • Figure 2. The distribution of the health centers, and the allocation of infected individuals to centers
  • Figure 3. Cumulative overcrowding distribution by people in household (blue) and bedrooms in house (red)
  • Figure 4. Histograms of geographic group sizes with corresponding superimposed gamma distributions for (a): the high MDR-TB prevalence cluster; and,(b): the high MDR-TB prevalence cluster
  • Figure 5. Bayesian random effect components for the clusters of aggregated individuals based INH, RIF, and EMB for a SSRE high MDR-TB prevalence clusters
  • Figure 6. Bayesian random effect components for the clusters of aggregated individuals based INH, RIF, and EMB for a SSRE for the MDR-TB prevalence cluster