Models in MARK
Slide 6 of 139
Go to slide:
1: Program MARK
2: Advances since 1985
3: Encounter Techniques
4: Types of Encounter History Data
5: Estimable Parameters
6: Models in MARK
7: Additional Features
8: Knowledge Required of the User
9: Data are Required to Gain Reliable Knowledge
10: Computer Requirements
11: Program Architecture
12: Vocabulary of MARK
13: Basic Data
14: Likelihood Function
15: Pr(Enc. History)
16: Dead Encounters
17: Seber 1970
18: Brownie et al. 1985 Model
19: Dead Encounters
20: Dead Encounters
21: Dead Encounters
22: Dead Encounters
23: Dead Encounters
24: Dead Encounters
25: Dead Encounters
26: Dead Encounters
27: Dead Encounters
28: Dead Encounters
29: Dead Encounters
30: Live Encounters (CJS)
31: Live Encounters (CJS)
32: Live Encounters (CJS)
33: Live Encounters (CJS)
34: Live Encounters (CJS)
35: Live Encounters (CJS)
36: Live Encounters (CJS)
37: Live Encounters (CJS)
38: Live Encounters (CJS)
39: Live Encounters (CJS)
40: Live Encounters (CJS)
41: Extensions to CJS
42: Multi-Strata Model
43: Jolly-Seber Model
44: Robust Design
45: Robust-design Multi-strata Models
46: Joint Encounters
47: Joint Encounters
48: Joint Encounters
49: Joint Encounters
50: Joint Encounters
51: Barker Live-Dead Model
52: Robust Design Barker Model
53: Robust Design Barker Model
54: Multi-strata with Live and Dead Encounters
55: Joint Encounters: Estimation of Radio Effects
56: Closed Captures
57: Closed Captures
58: Closed Captures
59: Closed Captures
60: Closed Captures
61: Closed Captures
62: Closed Captures
63: Closed Captures
64: Closed Captures
65: Closed Captures with Heterogeneity
66: Closed Captures with Heterogeneity
67: Closed Captures
68: Closed Captures
69: Known Fate
70: Known Fate
71: Known Fate
72: Known Fate
73: Known Fate
74: Known Fate
75: Known Fate Equivalent to Kaplan Meier
76: Advantages of Using MARK Known Fate over Kaplan-Meier
77: Model Building
78: Parameter Index Matrix PIM
79: Parameter Index Matrix PIM
80: Parameter Index Matrix PIM
81: Parameter Index Matrix PIM
82: Parameter Index Matrix PIM
83: Parameter Index Matrix PIM
84: Parameter Index Matrix PIM
85: Parameter Index Matrix PIM
86: PIM Manipulation
87: PIM Chart
88: Design Matrix
89: Individual Covariates
90: PIMs for Design Matrix Examples
91: Model {theta(g*t)}
92: Link Functions
93: Design Matrix
94: Design Matrix
95: Design Matrix
96: Logit vs. Real Parameters
97: Model {theta(.)}
98: Model {theta(g)}
99: Model {theta(t)}
100: Model {theta(g + t)}
101: Model {theta(g*t)}
102: Model {theta(T)}
103: Model {theta(g + T)}
104: Model {theta(g*T)}
105: Design Matrix Manipulation
106: Design Matrix Functions
107: Numerical Estimation
108: Numerical Methods
109: Features of Results Browser
110: Hypothesis Tests
111: Goodness-of-fit Procedures
112: Advanced Analyses
113: Model Selection
114: Model Selection Information-Theoretic Approach
115: Model Selection
116: Selecting Best Model
117: Model Averaging
118: Model Averaging
119: Model Averaging
120: Model Averaging
121: Variance Components
122: Process Variance 2
123: Variance Components
124: Variance Components
125: Variance Components
126: Example
127: Example Continued
128: Program MARK Assumptions
129: Quasi-likelihood Estimation
130: Goodness of Fit
131: Goodness of Fit Logistic Regression
132: Goodness-of-fit Testing
133: Bootstrap Procedure
134: Goodness of Fit Bootstrap Approach
135: Logistic Regression Procedure
136: Logistic Regression Procedure
137: Logistic Regression Procedure
138: Program Documentation
139: Program Availability
