A class of saturated row–column designs

A new class of row–column designs is proposed. These designs are saturated in terms of eliminating two-way heterogeneity with an additive model. The (m,s)-criterion is used to select optimal designs. It turns out that all (m,s)-optimal designs are binary. Square (m,s)-optimal designs are constructed and they are treatment-connected. Thus, all treatment contrasts are estimable regardless of the row and column effects.     

Bayesian clustering for row effects models

We deal with two-way contingency tables having ordered column categories. We use a row effects model wherein each interaction term is assumed to have a multiplicative form involving a row effect parameter and a fixed column score. We propose a methodology to cluster row effects in order to simplify the interaction structure and to enhance the interpretation of the model. Our method uses a product partition model with a suitable specification of the cohesion function, so that we can carry out our analysis on a collection of models of varying dimensions using a straightforward MCMC sampler. The methodology is illustrated with reference to simulated and real data sets.     

Additional comparisons of designs to estimate variance components in a two-way classification model

In this paper, the research of Muse and Anderson is extended to include additional comparisons of designs, featuring planned unbalance, for the estimation of variance components in a two-way cross classification model. Their results are extended to Include the following: (i) a small sample study of the original off-diagonal (OD) design and (ii) an asymptotic maximum likelihood investigation of three modifica-tions of the balanced diagonal rectangles (BD) design and one modification of the 01) design to permit the estimation of row, column, interaction and error variance components. Also a general iterative least.     

Row-column designs with minimal units

A new class of row-column designs is proposed. These designs are saturated in terms of eliminating two-way heterogeneity with an additive model. The proposed designs are treatment-connected, i.e., all paired comparisons of treatments in the designs are estimable in spite of the existence of row and column effects. The connectedness of the designs is justified from two perspectives: linear model and contrast estimability. Comparisons with other designs are studied in terms of A-, D-, E-efficiencies as well as design balance.     

Estimation of proportions for multinomial contingency tables subject to marginal constraints

For a two-way table of observed proportions we consider four approaches to the problem of fitting a new set of proportions which-subject to satisfying marginal constraints on row and column totals-corresponds as closely as possible to the original table, The approaches are known to be asymptotically equivalent; but our chief interest is in establishing, at least for the 2x2 table, a hierarchy of preferences under multinomial sampling for moderate sample sizes. Implementation of minimum chi-square is described and recommended.     

On the use of diagnostic biplots in model screening for genotype by environment tables

Popular rank-2 and rank-3 models for two-way tables have geometrical properties which can be used as diagnostic keys in screening for an appropriate model. Row and column levels of two-way tables are represented by points in two or three dimensional space, whereupon collinearity and coplanarity of row and column points provide diagnostic keys for informal model choice. Coordinates are obtained from a factorization of the two-way table Y in the matrix product UV ^T. The rows of U then contain row-point coordinates and the rows of V column-point coordinates. Illustrations of applications of diagnostic biplots in the literature were restricted to data from chemistry and physics with little or no noise. In plant breeding, two-way tables containing substantial amounts of noise regularly arise in the form of genotype by environment tables. To investigate the usefulness of diagnostic biplots for model screening for genotype by environment tables, data tables were generated from a range of two-way models under the addition of various amounts of noise. Chances for correct diagnosis of the generating model depended on the type of model. Diagnostic biplots on their own do not seem to provide a sufficient means for model selection for genotype by environment tables, but in combination with other methods they certainly can provide extra insight into the structure of the data.     

A Hybrid Method for Random Pattern Sequence Classification

We present a hybrid method for random pattern sequence classification that takes into account the random structural properties of the sequence. The method works in two steps. A segmentation step, dividing the original sequence into segments, such that all observations in a same segment belong to a unique class, and a classification step, where each segment is classified by a neural network classifier.     

Estimation of parameters in latin squares and graeco-latin squares with missing observations

We show that if in an additive model with p-2 MOLS if one omits up to p-1 observations from the same row, the same column or which correspond to the same letter in any of the squares all effects are estimable. On the other hand with only two missing observations not from the same row, the same column or corresponding to the same letter in any of the squares one degree of freedom is lost for each set of effects.     

Bootstrap confidence regions for correspondence analysis

A bootstrap-based method for constructing confidence regions (CRs) around row or column points projected onto a pair of axes from the correspondence analysis (CA) of a two-way contingency table is presented. These regions deal with the specific question of the sampling variation of sample row and column profile points around population row and column profile points when both are projected onto the observed axes, rather than the decomposition of the χ²-test of independence or the general question of the stability of the observed CA display which has been considered in previous work. The method therefore constructs the regions in a different way to what has been proposed before. A simulation experiment shows that the method performs well in most of the situations in which it might be used, with a few exceptions being noted. An example illustrates that the method produces conclusions which are consistent with those from detailed parametric modelling.     

Clustering for contingency tables: boxes and partitions

The correspondence analysis (CA) method appears to be an effective tool for analysis of interrelations between rows and columns in two-way contingency data. A discrete version of the method, box clustering, is developed in the paper using an approximation version of the CA model extended to the case when CA factor values are required to be Boolean. Several properties of the proposed SEFIT-BOX algorithm are proved to facilitate interpretation of its output. It is also shown that two known partitioning algorithms (applied within row or column sets only) could be considered as locally optimal algorithms for fitting the model, and extensions of these algorithms to a simultaneous row and column partitioning problem are proposed.     

An analysis of the effects of atmospheric sulphate and ammonium on soil solution chemistry using median polishing

Soils from locations near Amsterdam, Dublin, Exeter (SW England), Göttingen, Paris and in the Grizedale Forest (NW England) were placed in lysimeters in a Norway spruce (Picea abies) stand in the Grizedale Forest and the soil solutions, rainfall and throughfall were monitored for 54 weeks. Two-way data tables for SO4, NH4, NO3, Al, Mg, Ca and pH were analysed by median polishing, in which each observed data value is decomposed into a typical value plus a row effect plus a column effect plus a residual. The row effects suggested that SO4 was the main acidifying influence in the Göttingen soil, but in the other soils SO4 had less effect than did NO3. The column effects showed little variation over the 54 weeks. Low values for NO3 in March-May and September were associated with periods of low or zero rainfall. The residuals contained from 27% (Al) to 59% (NO3) of the variation. Some were interpretable and contained useful information. Where the residuals are considered to represent uninterpretable random variation (‘noise’) they can be subtracted from the original data to give corrected data for other types of analysis. In median polishing, changing a few values in the input data, even drastically, will result in unusual residuals only in the cells with altered values. Hence, it is a useful technique for screening two-way data tables obtained from field experiments.     

Wavelet-based gradient boosting

A new data science tool named wavelet-based gradient boosting is proposed and tested. The approach is special case of componentwise linear least squares gradient boosting, and involves wavelet functions of the original predictors. Wavelet-based gradient boosting takes advantages of the approximate \(\ell _1\) penalization induced by gradient boosting to give appropriate penalized additive fits. The method is readily implemented in R and produces parsimonious and interpretable regression fits and classifiers.     

A class of distribution-free tests for two-way layout

In this article a class of distribution-free tests for the hypothesis of no row (treatment) effect in a two-way layout design, with several observations per cell, is proposed. The tests are based on U-statistics, constructed by considering minima of all possible subsamples of same size from each cell.The proposed class of tests is compared with the parametric test, Mack and Skillings test and Yate's test for two-way layout, in terms of Pitman ARE sense. It is seen that for the case of equal number of observations per cell, the proposed tests have better efficiency for exponential and uniform error distributions.     

A DISTRIBUTION-FREE TEST FOR TWO-WAY LAYOUT1

Many nonparametric tests have been proposed for the hypothesis of no row (treatment) effect in a one-way layout design. Examples of such tests are Kruskal-Wallis H-test, Bhapkar's (1961) V-test and Deshpande's (1965) L-test. However not many tests are available for testing the same hypothesis in a two-way layout design without interaction. Perhaps the only “established” test is the one due to Friedman (1937). However, it applies to the case of one observation per cell only. In this paper, a new distribution-free test is proposed for the hypothesis of row effect in a two-way layout design. It applies to the case of several observations per cell, not necessarily equal. The asymptotic efficiency of the proposed test relative to other tests is studied.     

Testing additivity in two-way classifications with no replications:the locally best invariant test

Row x column interaction is frequently assumed to be negligible in two-way classifications having one observation per cell. Absence of interaction allows the researcher to estimate experimental error and to proceed with making inferences about row and column effects. If additivity is suspect, it is conventional to test it against a structured alternative. If the structured alternative missspecifies the existing nonadditivity, then the power of the test is low, even if the magnitude of the existing nonadditivity is large. The locally best invariant (LBI) test of additivity is less subject to model misspecification because a particular structural alternative need not be hypothesized. This paper illustrates the LBI test of additivity and compares its power to that of the Johnson-Graybill likelihood ratio (LR) test. The LBI test performs as well as the LR test under a Johnson-Graybill alternative and performs better than the LR test under more general alternatives.     

On Sensitivity of Inverse Response Plot Estimation and the Benefits of a Robust Estimation Approach

Abstract. Inverse response plots are a useful tool in determining a response transformation function for response linearization in regression. Under some mild conditions it is possible to seek such transformations by plotting ordinary least squares fits versus the responses. A common approach is then to use nonlinear least squares to estimate a transformation by modelling the fits on the transformed response where the transformation function depends on an unknown parameter to be estimated. We provide insight into this approach by considering sensitivity of the estimation via the influence function. For example, estimation is insensitive to the method chosen to estimate the fits in the initial step. Additionally, the inverse response plot does not provide direct information on how well the transformation parameter is being estimated and poor inverse response plots may still result in good estimates. We also introduce a simple robustified process that can vastly improve estimation.     

CONSTRUCTION OF RESOLVABLE ROW-COLUMN DESIGNS BASED ON CONTRACTIONS

This paper defines the contraction of a resolvable row‐column design for more than two replicates. It shows that the (M,S)‐optimality criterion for the row‐column designs can be expressed simply in terms of the elements of the row and column incidence matrices of the contraction. This allows the development of a very fast algorithm to construct optimal or near‐optimal resolvable row‐column designs. The performance of such an algorithm is compared with an existing algorithm.     

Positive association in a two-way contingency table:likelihood ratio tests

Given a two-way contingency table in which the rows and columns both define ordinal variables, there are many ways in which the informal idea of positive association between those variables might be defined. This paper considers a variety of definitions expressed as inequality constraints on cross-product ratios. Logical relationships between the definitions are explored. Each definition can serve as a composite alternative against which the null hypothesis of no association may be tested. For a broad class of such alternatives a decomposition of the log-likelihood gives both an explicit likelihood ratio statistic and its asymptotic null hypothesis distribution. Results are derived for multinomial sampling and for fully conditional sampling with row and column totals fixed.     

TWO-WAY CHANGE-POINT MODEL AND ITS APPLICATION

Exact null and alternative distributions of the two-way maximally selected x² for interaction between the ordered rows and columns are derived for each of the normal and Poisson models, respectively. The method is one of the multiple comparison procedures for ordered parameters and is useful for defining a block interaction or a two-way change-point model as a simple alternative to the two-way additive model. The construction of a confidence region for the two-way change-point is then described. An important application is found in a dose-response clinical trial with ordered categorical responses, where detecting the dose level which gives significantly higher responses than the lower doses can be formulated as a problem of detecting a change in the interaction effects.     

Marginal inhomogeneity models for square contingency tables with nominal categories

For the analysis of square contingency tables with nominal categories, this paper proposes two kinds of models that indicate the structure of marginal inhomogeneity. One model states that the absolute values of log odds of the row marginal probability to the corresponding column marginal probability for each category i are constant for every i. The other model states that, on the condition that an observation falls in one of the off-diagonal cells in the square table, the absolute values of log odds of the conditional row marginal probability to the corresponding conditional column marginal probability for each category i are constant for every i. These models are used when the marginal homogeneity model does not hold, and the values of parameters in the models are useful for seeing the degree of departure from marginal homogeneity for the data on a nominal scale. Examples are given.