Danish et al., 2017 - Google Patents
Optimum stratification using mathematical programming approach: a reviewDanish et al., 2017
View PDF- Document ID
- 14604837368426191781
- Author
- Danish F
- Rizvi S
- Sharma M
- Jeelani M
- Publication year
- Publication venue
- Journal of Statistics Applications & Probability Letters
External Links
Snippet
The stratification technique which results in minimum possible variance is called optimum stratification. The main objective of stratification is to give a better cross-section of the population so as to gain a higher degree of relative precision. The strata may be constructed …
- 238000000034 method 0 abstract description 35
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jackson et al. | PHRAPL: phylogeographic inference using approximate likelihoods | |
Crawford et al. | Parameter tuning of a choice-function based hyperheuristic using particle swarm optimization | |
Zeng et al. | Design of fractional order PID controller for automatic regulator voltage system based on multi-objective extremal optimization | |
Oldewurtel et al. | Stochastic model predictive control for building climate control | |
Ahuja et al. | Some recent advances in network flows | |
Czajkowski et al. | Steering the interpretability of decision trees using lasso regression-an evolutionary perspective | |
Danish et al. | Optimum stratification using mathematical programming approach: a review | |
Burrello et al. | Enhancing neural architecture search with multiple hardware constraints for deep learning model deployment on tiny iot devices | |
Hu et al. | An efficient robust optimization method with random and interval uncertainties | |
Awais et al. | An MCTS-based framework for synthesis of approximate circuits | |
Duarte et al. | The river network toolkit–RivTool | |
Turner et al. | Generic sequential sampling for metamodel approximations | |
Webb et al. | How does network structure and complexity in river systems affect population abundance and persistence? | |
CN109767034B (en) | Method, device, computer equipment and storage medium for setting value optimization of relay protection | |
Pathak et al. | Traveling salesman problem using bee colony with SPV | |
WO2003021489A2 (en) | Method of robust technology design using rational robust optimization | |
Sandes et al. | Formalization of block pruning: Reducing the number of cells computed in exact biological sequence comparison algorithms | |
Wang et al. | Learning to Optimise Climate Sensor Placement using a Transformer | |
CN107491841A (en) | Nonlinear optimization method and storage medium | |
US6807652B2 (en) | Method of robust semiconductor circuit products design using rational robust optimization | |
Xiao et al. | Experimental designs for precise parameter estimation for non-linear models | |
Schaechtle et al. | Probabilistic programming with gaussian process memoization | |
Lotov et al. | Launch pad method in multiextremal multiobjective optimization problems | |
Harris et al. | Occupancy is nine-tenths of the law: occupancy rates determine the homogenizing and differentiating effects of exotic species | |
Zhu et al. | Speeding up a memetic algorithm for the max-bisection problem |