| One- and two-sided tolerance intervals for general balanced mixed models and unbalanced one-way random models |
520:Y |
QCAS / 52 / 2 / 211 |
| New frontiers in the design of experiments |
520:Y |
QCAS / 52 / 1 / 79 |
| Identifying need for new factors in EVOP |
520:Y |
QCAS / 51 / 6 / 663 |
| Response modeling methodology (RMM) – maximum likelihood estimation procedures |
520:Y |
QCAS / 51 / 5 / 553 |
| A game theory application in robust design |
520:Y |
QCAS / 51 / 4 / 421 |
| A new approach for multiple-response optimization |
520:Y |
QCAS / 51 / 3 / 309 |
| Large factorial designs for product engineering and marketing research applications |
520:Y |
QCAS / 51 / 3 / 311 |
| Experimental designs in the high added value products industry |
520:Y |
QCAS / 51 / 3 / 315 |
| Design of experiments for dummies |
520:Y |
QCAS / 51 / 1 / 75 |
| Optimization of correlated multiple quality characteristics using desirability function |
520:Y |
QCAS / 51 / 1 / 77 |
| Dual-response surface optimization: A weighted MSE approach |
520:Y |
QCAS / 50 / 6 / 685 |
| Designing experiments for causal networks |
520:Y |
QCAS / 50 / 4 / 439 |
| Simple pilot procedures for the avoidance of disconnected experimental designs |
520:Y |
QCAS / 49 / 5 / 559 |
| Factorial experiments when factor levels are not necessarily reset |
520:Y |
QCAS / 49 / 5 / 561 |
| Selecting 2m-p designs using a minimum aberration criterion when some two-factor interations are important m-p is superscript |
520:Y |
QCAS / 49 / 4 / 447 |
| Recognition and importance of restrictions on randomization in industrial experimentation |
520:Y |
QCAS / 49 / 4 / 451 |
| Constructing meta-models for computer experiments |
520:Y |
QCAS / 49 / 3 / 321 |
| Analysis of supersaturated designs |
520:Y |
QCAS / 49 / 2 / 187 |
| A review and analysis of the Mahalanobis-Taguchi system |
520:Y |
QCAS / 49 / 2 / 195 |
| Orthogonal arrays for experiments with lean designs |
520:Y |
QCAS / 49 / 1 / 87 |
| Application of analysis of means (ANOM)to nested designs for improving the visualization and understanding of the sources of variation of chemical and pharmaceutical processes |
520:Y |
QCAS / 48 / 6 / 661 |
| Operating window experiments: A novel approach to quality improvement |
520:Y |
QCAS / 48 / 6 / 665 |
| Design optimization using ANOVA |
520:Y |
QCAS / 48 / 5 / 549 |
| Minimal design augmentation schemes to resolve complex aliasing in industrial experiments |
520:Y |
QCAS / 48 / 5 / 551 |
| A case study in design of experiments: Improving the manufacture of vicose fiber |
520:Y |
QCAS / 48 / 4 / 433 |
| Using Taguchi methods to improve a control scheme by adjustment of changeable settings: A case study |
520:M |
QCAS / 48 / 3 / 309 |
| Follow-up designs to resolve confounding in multifactor experiments |
520:Y |
QCAS / 48 / 3 / 311 |
| The role of statistical design of experiments in Six Sigma: Perspectives of a practitioner |
520:Y |
QCAS / 48 / 3 / 313 |
| The role of statistical design of experiments in Six Sigma: Perspectives of a practioner |
520:Y |
QCAS / 48 / 2 / 195 |
| A two-stage Bayesian model selection strategy for supersaturated designs |
520:Y |
QCAS / 48 / 2 / 197 |
| Experimental designs when there are one or more factor constraints |
520:Y |
QCAS / 47 / 5 / 545 |
| Switching-one-column follow-up experiments for Plackett-Burman designs |
520:Y |
QCAS / 47 / 5 / 549 |
| Sequence-leveled experimental designs at work |
520:Y |
QCAS / 47 / 5 / 551 |
| Optimal split-plot designs |
520:Y |
QCAS / 47 / 4 / 429 |
| Optimal exact experimental designs with correlated errors through a simulated annealing algorithm |
520:Y |
QCAS / 47 / 3 / 319 |
| Taguchi methods in American universities and corporations |
520:Y |
QCAS / 47 / 2 / 183 |
| A pragmatic approach to experimental design in industry |
520:Y |
QCAS / 47 / 2 / 187 |
| Design issues in fractional factorial split-plot experiments |
520:Y |
QCAS / 47 / 1 / 81 |
| Construction of third order slope rotatable designs through doubly balanced incomplete block designs |
520:Y |
QCAS / 47 / 1 / 83 |
| Experimental designs optimally balanced for trend |
520:Y |
QCAS / 46 / 6 / 685 |
| Quality improvement through design of experiments: A case study |
520:Y |
QCAS / 46 / 5 / 559 |
| Some new two-level saturated designs |
520:Y |
QCAS / 46 / 4 / 443 |
| Use of mathematical programming in the analysis of constrained and unconstrained industrial experiments |
520:Y |
QCAS / 46 / 3 / 315 |
| Putting Taguchi methods to work to solve design flaws |
520:Y |
QCAS / 46 / 2 / 199 |
| Experimental sequence: A decision strategy |
520:Y |
QCAS / 46 / 2 / 201 |
| Improving the yield of printed circuit boards using design of experiments |
520:M |
QCAS / 45 / 6 / 679 |
| Specification limit under a quality loss function |
520:Y |
QCAS / 45 / 6 / 681 |
| Using the Taguchi loss function to reduce common-cause variation |
520:Y |
QCAS / 45 / 6 / 683 |
| Experimental design for product and process design and development |
520:Y |
QCAS / 45 / 6 / 687 |
| Role of the organized sector in developing small-scale industries as vendors: A case study of experimental approach |
520:M |
QCAS / 45 / 5 / 565 |
| Considerations associated with restrictions on randomization in industrial experimentation |
520:Y |
QCAS / 45 / 5 / 567 |
| Optimization of Arc-PVD TiN coating process parameters by Taguchi technique |
520:Y |
QCAS / 45 / 5 / 569 |
| Taguchi parameter design with multiple quality characteristics |
520:Y |
QCAS / 45 / 5 / 571 |
| Critical values of the Lenth method for unreplicated factorial designs |
520:M |
QCAS / 45 / 3 / 325 |
| The analysis of designed experiments with nonnormal responses |
520:Y |
QCAS / 45 / 3 / 327 |
| Some risks in the construction and analysis of supersaturated designs |
520:Y |
QCAS / 45 / 2 / 201 |
| An integrated method of parameter design and tolerance design |
520:Y |
QCAS / 45 / 1 / 95 |
