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Model Type (模型分类)
1) Envelopment Model (包络模型)
2) Multiplier Model (乘数模型)
3) FDH 模型
Distance to measure efficiency (用于测量效率的距离类型)
1) Radial (CCR 1978; BCC 1984) (径向距离)
2) Maximum Distance to Frontier (ERM, Enhanced Russel Measure, Pastor, Ruiz, and Sirvent 1999; SBM, Slacks-based Measure, Tone 2001) (至强有效前沿的最远距离，即SBM模型)
3) Minimum Distance to Weak Efficient Frontier (Charnes, Roussea, and Semple1996) (至弱有效前沿的最近距离)
4) Minimum Distance to Strong Efficient Frontier (Closest Target), with full features: strong monotonicity algorithm; CRS, VRS, NIRS and NDRS; Non-oriented, Input-oriented and Output-oriented. (至强有效前沿的最近距离，MinDS距离)
Ref. to (J. Aparicio et al., 2007; G. R. Jahanshahloo et al., 2012; Gholam Reza Jahanshahloo, Roshdi, & Davtalab-Olyaie, 2013; Olyaie, Roshdi, Jahanshahloo, & Asgharian, 2014; J Aparicio et al., 2017; Zhu et al., 2018; Zhu et al., 2022)
5) Directional Distance Function (Chambers, Chung, and Färe 1996; Chung, Färe, and Grosskopf 1997) (方向距离函数)
Direction Vector can be (方向向量类型)
a): ( -|x0|, |y0|, -|b0| )'
b): ( -|x̅|, |y̅|, -|b̅| )'
c) Vector (1, 1, ..., 1)'
d): Range (RDM, Portela, Thanassoulis, and Simpson 2004)
e) Customized (same for all DMUs) (为所有DMU自定义相同的方向向量)
f) Customized (DMU specific) (为各DMU自定义不同的方向向量)
6) A Series of Weighted Additive Models (加权加性距离)
a) Simple Additive model: Weights = (1, 1, 1, ...) (不加权)
b) Normalized Weighted Additive (Lovell and Pastor 1995) (标准化权重)
c) Weights = 1/|x0|, 1/|y0|
d) Weights = 1/|x̅|, 1/|y̅|
e) Range Adjusted Measure (RAM, Cooper, Park, and Pastor 1999) (RAM模型)
f) Bounded Adjusted Measure (BAM, Cooper, Pastor, Borras, Aparicio, and Pastor 2011) (BAM模型)
g) Directional Slacks-based Measure (DSBM, Fukuyama and Weber 2009) (定向SBM模型)
h) Customized Weights (same for all DMUs) (为各DMU自定义相同的权重)
i) Customized Weights (DMU specific) (为各DMU自定义不同的权重，使用此类型实现一般化的“非径向方向距离函数”，即NDDF模型)
7) Hybrid Distance(Radial and SBM Measure): (EBM, Epsilon-based Meaure,Tone and Tsutsui 2010) (EBM模型)
8) Cost (成本效率)
9) Revenue (收益效率)
Orientation to measure efficiency (模型导向)
1) Input-oriented (投入导向)
2) Output-oriented (投入导向)
3) Non-oriented (非导向)
RTS to measure efficiency (规模报酬)
1) Constant returns to scale (CRS) (规模报酬不变)
2) Variable returns to scale (VRS) (规模报酬可变)
3) Non-increasing returns to scale (NIRS) (规模报酬非增)
4) Non-decreasing returns to scale (NDRS) (规模报酬非减)
5) Decomposition of Efficiency or TFP Index (效率或生产率指数分解)
TFP Index: Malmquist Index and Hicks-Moorsteen Index (also called HMB Index)
(全要素生产率指数: Malmquist指数和HMB指数)
a) Adjacent Malmquist (相邻参比)
b) Fixed Malmquist (固定参比)
c) Global Malmquist (全局参比)
d) Sequential Malmquist (序列参比)
e) Window-Malmquist (Adjacent) (相邻窗口参比)
f) Window-Malmquist (Fixed) (固定窗口参比)
g) Global with Sequential Malmquist (全局和序列参比)
TFP index decomposition: Efficiency Change (catch-up), Technological Change (frontier shift), Scale Efficiency Change, biased Technological Change, TC=OBTC*IBTC*MATC (Fare et al 1997)
Three types of indices can be computed: Index(t-1, t); Index (t-n, t) : n is user-defined; Index (t0, t): t0 is the initial period.
Window DEA (窗口DEA)
Cluster model (群组参比模型，广义DEA模型)
a) Self-benchmarking (自我参比)
b) Cross-benchmarking (交叉参比)
c) Downward-benchmarking (向下参比)
d) Upward-benchmarking (向上参比)
e) Lower-adjacent-benchmarking (下方临群参比)
f) Upper-adjacent-benchmarking (上方临群参比)
g) Window-benchmarking (窗口参比)
h) Fixed-benchmarking (固定参比)
Other models
1) Super-efficiency model (超效率模型)
2) Modified SBM (Sharp et al 2007) (MSBM模型)
3) Modified SBM (Lin et al 2019) (MSBM模型)
4) Cross efficiency model (交叉效率模型)
Second-stage methods are available (第2阶段方法):
Minimize/Maximize the trade balance of other DMUs as a whole
a) Blanket Benevolent (Type I in Doyle and Green 1995)
b) Blanket Aggressive (Type I in Doyle and Green 1995)
Maximize/Minimize the cross-efficiency of other DMUs as a whole
c) Blanket Benevolent (Type II in Doyle and Green 1995)
d) Blanket Aggressive (Type II in Doyle and Green 1995)
Maximize/Minimize the cross-efficiency of other DMUs as a whole
e) Blanket Benevolent (Ruiz (2013))
f) Blanket Aggressive (Ruiz (2013))
Maximize/Minimize the cross-efficiency of a customized virtual DMU
g) Benevolent (customized)
h) Aggressive (customized)
5) Game Cross Efficiency model: Nash Equilibrium model (Liang, et al 2008; Wu, et al 2009) (博弈交叉效率模型)
6) Undesirable outputs, desirable inputs (非期望产出(越少越好的产出) 和 期望投入(越多越好的投入))
7) Nondiscretionary input/output model (不可随意控制的投入产出)
8) Preference (weighted) model (Set Input/output Weights) (偏好(加权)模型，设置投入产出权重)
9) Restricted multiplier model (assurance region model, trade-offs between inputs and outputs) (权重比值约束模型)
What's more important, MaxDEA X provides nearly all possible combinations of the above models.
(最为重要的是, MaxDEA X 提供了尽可能多的上述模型选项的组合应用。)