Neilson, K.A., Towns, D.R., and Curran, J. (2004) **Assessing the habitat use of chevron skink ***(Oligosoma homalonotum)* on Great Barrier Island through trapping and radiotracking *New Zealand Journal of Zoology* 31, 106-107.

John, J.A. (2003) **On the contraction of a resolvable row-column design.** *12th International Workshop on Matrices and Statistics.* Dortmund, Germany.

Curran, J.M., Buckleton, J.S. and Triggs, C.M. (2002) **What is the Magnitude of the Subpopulation Effect?** 5th International Conference on Forensic Statistics, Venice, Italy.

Hunt, L.A. & Jorgensen, M.A. (2002) **Unsupervised Learning from Incomplete Data using a Finite Mixture Model Approach** Statistical Data Mining & Knowledge Discovery Conference, Knoxville, Tennessee.

Jorgensen, M.A. (2002) **Evaluation of the Asymptotic Variance-Covariance Matrix for Finite Mixture Distributions.** 13th Annual Meeting of the Statistical Society of Canada, Hamilton, Ontario.

Jorgensen, M.A. (2002) **Measurement Error Comparison using Single Factor Analysis with Minimum Message Estimation.** Mixture Models, Bump-Hunting and Measurement Errors Workshop, Cleveland USA.

Jorgensen, M.A. (2002) **Using Finite Mixtures to Robustify Statistical Models.** International Conferfence on Robust Statistics ICORS, Vancouver, Canada AND 16th Australian Statistical Conference.

McWhirter, J.L. (2002) **Bayesian Modelling of Pulsatile Data.** 16th Australian Statistical Conference, Canberra.

Bolstad, W.M. (2001) **Making the 21st Century Bayesian: Teaching Bayesian Statistics at First Year Level** *ISBA Bulletin.*

Hunt, L.A. & Jorgensen, M.A. (2001) **Mixture Model Clustering for Mixed Categorical and Continuous Data with Missing Values.** Mixtures 2001 Conference, Hamburg, Germany

Bolstad, W.M. (1997) **Monte Carlo Methods in Bayesian Statistics** *The New Zealand Statistician.* Vol. 32, 2-22.

Henderson, H.V. (1997) **Calf disbudding** *Dairy Cattle Veterinarians* 14, 5.

Littler, R. A, and Whitaker, D. (1997) **Estimating staffing requirements at an airport terminal** *J Operational Research Soc* 48, 124-131.

Mackle, T.R., Petch S.F., Bryant A.M., Auldist, M.J., Henderson H.V., MacGibbon A.K.H. (1997) **Variation in the characteristics of milkfat from pasture-fed dairy cows during late spring and the effects of grain supplementation ** *New Zealand Journal of Agricultural Research* 40, 349-359.

Triggs, C.M., Curran, J.M., Buckleton, J.S. and Walsh, K.A.J. (1997) **The grouping problem in forensic glass analysis: a divisive approach** *Forensic Science International* 85, 1-14.

Bolstad, W.M. (1996) **Comments on: Paradox or Paradigm** *The New Zealand Statistician.* Vol. 31 7-9.

Dale, J. (1996) **Contribution to the Discussion of Lee, Y. And Nelder, J.A. 'Hierarchical generalized linear models.'** *Journal of the Royal Statistical Society Series B* 58 No. 4, 665.

Eccleston, J.A. and John, J.A. (1996) **Orthogonal main effect plans for two and three factors in small blocks** *Metrika* 43, 203-211.

Williams, E.R. and John, J.A. (1996) **A note on optimality in lattice square designs ** *Biometrika *83, 709-13.

Bolstad, W.M. (1995) **The Multiprocess Dynamic Poisson Model** *Journal of the American Statistical Association*
90, 429 227-232.

Curran, J.M. (1995) **Statistical analysis of forensic glass evidence: a review ** *Proceedings of the A.C. Aitken Centenary Conference, Dunedin* 95-102.

John, J.A. (1995) **Interchange algorithms for constructing designs with complex blocking structures** Statistical Theory and Applications, Papers in Honour of H A David (Ed H N Nagaraja, P K Sen and D F Morrison) 233-246.

Curran, J.M. (1994) **A method for adjusting for unit non-response ** *Proceedings of the NZSA/NZOR Conference, Palmerston North* 291-296.

Bolstad, W.M. (1993) **Finding the Initial Prior Distribution in the Multiprocess Dynamic Linear Trend Model with
Dummy Seasonal Effects by Backcasting in the Time-reversed Model** *ASA Proceedings Business and Economics Section.*

Eccleston, J., John, J.A. and Whitaker, D. (1993) **Some row-column designs with adjusted orthogonality** *J of Statist Planning and Inference* 36.

John, J.A. and Whitaker, D. (1993) **Construction of resolvable row-column designs using simulated annealing** *Australian J Statist* 35, 237-245

John, J.A., Whitaker, D. and Triggs, C.M. (1993) **Construction of cyclic designs using integer programming** *J Statist Planning and Inference* 36, 357-366.

Bolstad, W.M. (1992) **Multiprocess Dynamic Linear Model for Statistical Process Control** *ASA Proceedings Quality and
Productivity Section.*

Bolstad, W.M. (1992) **Robust Bayesian estimation using a variable mixture of conjugate family members** *The New Zealand Statistician*
Vol. 27 15-22.

Jorgensen, M., Zhaorong, J and C.A. McGilchrist (1992) **Mixed model discrete regression** *Biometrical Journal *34, 691-700.

Whitaker, D, and Brown, S.M. (1991) **Linear programming modelling with databases, ** *Computers and Operations Research*, Vol. 18, No. 1, pp33-41.

Bolstad, W.M. (1990) **The efficiency of dynamic linear model estimators applied to a linearly aggregated time series** *Comm. in Statistics Theory and Methods* 19(1), 83-90.

Buwalda, J.G., Wilson, G.J., Smith, G.S. and Littler, R.A. (1990) **The development and effects of nitrogen deficiency in field grown kiwifruit (actinidia deliciosa) vines** *Plant and Soil *v 129.

Jorgensen, M. (1990) **Implicitly defined statistics ** *American Statistician* 44, letters.

Whitaker, D. and Cammell, S. (1990) **A partitioned cutting-stock problem applied in the Meat Industry.** *Journal of the Operational Research Society*, Vol. 41, No. 9, pp801-807.

Whitaker, D., Triggs, C.M. and John, J.A. (1990) **Construction of block designs using mathematical programming** *Journal of the Royal Statistical Society* B, 52(3) 497-503.

Douglas, J.A., Follett, J.M. and Littler, R.A. (1989) **Boron requirements of asparagus seedlings grown in sand culture ** *Scientia Horticulturae * 38, 33-42.

McWhirter,J., Littlejohn, R.P., Henderson, H.V., Thompson, J.F., Montgomery, G.W. and MacMillan, K.L. (1989) **Analysing Hormone Profiles with Pulses** *New Zealand Statistician *24 50-56.

Sarathchandra, S.U., Perrott, K.W. and Littler, R.A. (1989) **Soil microbial biomass: influence of simulated temperature changes on size, activity and nutrient-content** *Soil Biology and Biochemistry * 21, 987-993.

Bolstad, W.M. (1988) **Estimation in the multiprocess dynamic generalized linear model** *Comm. in Statistics: Theory and Methods.*
17(12), 4179-4204.

Bolstad, W.M. (1988) **The multiprocess dynamic linear model with biased perturbations: a real time model for growth hormone level
** *Biometrika.* 75(4), 685-692.

Douglas, J.A., Littler, R.A. and Slay, M.J. (1988) **Effect of superphosphate with and without nitrogen on grain yield, grain size, nitrogen, phosphorus and sulphur concentrations, and baking quality of 'Karamu' wheat** *NZ J Agricultural Research* 31, 169-177.

Powell, C.L., Caldwell, K.I., Littler, R.A. and Warrington, I. (1988) **Effect of temperature regime and nitrogen fertiliser level on vegetative and reproductive bud development in Cymbidium orchids** *J American Society Horticultural Science *.

Bolstad, W.M. (1987) **An Estimation Method for the Seemingly Unrelated Regression Model with Contemporaneous Covariances based on an Efficient
Recursive Algorithm** *Communications in Statistics, Simulation and Computation.* Vol. 16. No 3 689-698.

Ledgard, S.F., Brier, G.S. and Littler, R.A. (1987) **Legume production and nitrogen fixation in hill pasture communities** *NZ J Agricultural Research* 30, p413-421.

Steele, K.W. and Littler, R.A. (1987) **Field evaluation of some factors affecting estimation of nitrogen fixation in pastures by 15N isotope dilution** *Australian J Agricultural Research *38, 153-161.

Bolstad, W.M. (1986) **An Efficient Algorithm for Harrison-Stevens Forecasting using the Multiprocess Dynamic Linear Model
** * Communications in Statistics, Simulation and Computation.* Vol. 15 No. 3 819-828.

Bolstad, W.M. (1986) **Harrison-Stevens Forecasting and the Multiprocess Dynamic Linear Model** *The American Statistician*
Vol 40. No. 2 129-136.

Jorgensen, M. (1986) **Design of a database for a single-release mark-recapture experiment** *Proceedings of the Pacific Statistical Conference*, 345-348.

Hill, P. (1985) **Kernel estimation of a distribution function** *Comm. in Statist. Theor. & Meth.* A14, 605-620.

Jorgensen, M. (1985) **Comparison of routine analyses by different laboratories** *New Zealand Statistician* 20 35- 43.

Hill, P. (1984) **Some thoughts on reference ranges
** *N.Z. J. Med. Lab. Tech.* 38, 69-73.

Jorgensen, M. (1981) **Fitting animal growth curves. ** *New Zealand Statistician * 16(2) 5- 15.

Littler, R.A. and Good, A.J. (1978) **Ages, extinction times and first passage probabilities for a multi-allele diffusion model with irreversible mutation** *Theoretical Population Biology *13, 214- 225.

Littler, R.A. and Good, A.J. (1978) **Fixation times and probabilities for an independent loci model in genetics** *Theoretical Population Biology *14, 204-214.

Jorgensen, M. (1975) **Regular ultrafilters and long ultrapowers** *Canadian Mathematical Bulletin *18, 41-43.

Littler, R.A. (1975) **Loss of variability at one locus in a finite population** *Mathematical Biosciences* 25 151-163.

Littler, R.A. and Fackerell. E.D. (1975) **Transition densities for neutral multi-allele diffusion
models** *Biometrics *31, 117-123.

Fackerell, E.D. and Littler, R.A. (1974) **Polynomials biorthogonal to Appell's polynomials** *Bulletin Australian Mathematical Society*11, 181-195.

Littler, R.A. (1973) **Linkage disequilibrium in two-locus finite random-mating models without selection or mutation** *Theoretical Population Biology * 4, 259-275.

Jorgensen, M. and A. Adler (1972) **Descendingly incomplete ultrafilters and the cardinality of ultrapowers ** *Canadian J. Math. *24, 830- 834.

Jorgensen, M. (1970) **An equivalent form of Lévy's axiom schema ** *Proc. Amer. Math. Soc.* 26 651-654.

Bradshaw, B., Dell., P.M., Healy, T., and Bolstad, W.M. **Inner shelf dynamics on a storm dominated coast, East Coromandel, New Zealand
** *Journal for Coastal Research* 7(1), 11-30.

Lee, Y. MacEachern, S.N., and Jung, Y. (2012) *Regularization of Case-Specific Parameters for Robustness and Efficiency* Statistical Science, 27, 350-372

Regularization methods allow one to handle a variety of inferential problems where there are more covariates than cases. This allows one to consider a potentially enormous number of covariates for a problem. We exploit the power of these techniques, supersaturating models by augmenting the “natural” covariates in the problem with an additional indicator for each case in the data set. We attach a penalty term for these case-specific indicators which is designed to produce a desired effect. For regression methods with squared error loss, an ℓ1 penalty produces a regression which is robust to outliers and high leverage cases; for quantile regression methods, an ℓ2 penalty decreases the variance of the fit enough to overcome an increase in bias. The paradigm thus allows us to robustify procedures which lack robustness and to increase the efficiency of procedures which are robust.
We provide a general framework for the inclusion of case-specific parameters in regularization problems, describing the impact on the effective loss for a variety of regression and classification problems. We outline a computational strategy by which existing software can be modified to solve the augmented regularization problem, providing conditions under which such modification will converge to the optimum solution. We illustrate the benefits of including case-specific parameters in the context of mean regression and quantile regression through analysis of NHANES and linguistic data sets.

Lee, Y., Lee, I., Jung, Y., McConkey, D., and Czerniak, B (2012) **In-Frame cDNA library combined with protein complementation assay identified ARL11-binding partners.** PLoS ONE, 7(12): e52290. doi:10.1371/journal.pone 0052290

O'Neil, T.A., Balks, M.A., Lopez-Martinez, J. and McWhirter, J.L. (2012) *A method for assessing the physical recovery of Antarctic desert pavements following human-induced disturbances: A case study in the Ross Sea region of Antarctica.** Journal of Environmental Management pp415-428*

*Paul, W.J., Hamilton , D.P., Ostrovsky, I, Miller, S.D., Zhang, A., and Muraoka, K. (2012) **Catchment land use and trophic state impacts on phytoplankton composition: a case study from the Rotorua lakes district, New Zealand* Hydrobiologia, The International Journal of Aquatic Service, Springer Science+Business Media, 698, 133-146.

*Elbohouty, M., Wilson, M.T., Voss, L.J., Steyn-Ross, D.A. and Hunt, L.A. (2013) ***In vitro electrical conductivity of seizing and non-seizing mouse brain slices at 10 kHz** Phys. Med. Biol. 58. 3599-3613.

*Phillips, P.M., Phadnis, J., Willoughby, R. and Hunt, L.A. (2013) ***Posterior Sloping Angle as a Predictor of Contralateral Slip in Slipped Capital Femoral Epiphysis** J. Bone Joint Surg. Am., 2013 Jan 16;95(2):146-150. doi: 10.2106/JBJS.L.00365

*Abell, J.M., Ozkundakci, D., Hamilton, D.P., and Miller, S.D. (2011) ***Relationships between land use and nitrogen and phosphorus in New Zealand lakes** Marine & Freshwater Research, 62 (2), 162-175

*Haslett, J., Joshi, C. and Geveli, R.V. (2011) **On efficient estimation of the variance of the Randomised Quasi Monte Carlo estimate* TCD-Statistifcs-11-08. trinity College, Dublin, p.1.13.

*Jorgensen, M.A. & McLachlan, G.J. (2008) ***Wallace's approach to upsupervised learning: the Snob program** The Computer Journal 51, pp571-578.

*Zuma, K., Jorgensen, M., Lurie, M. (2007) ***Analysis of interval-censored data from circular migrant and non-migrant sexual partnerships using the EM algorithm.** *Statistics in Medicine* 2007; 26, 309-319.

*Domijan, K., Jorgensen, M.A. & Reid, J. (2006) ***Semi-mechanistic modelling in nonlinear regression: a case study (with discussion).** *Australian and New Zealand Journal of Statistics *(48), pp373-392.

*Bill, M., Gill, P., Curran, J., Clayton, T., Pinchin, R, Healy, M. and Buckleton, J. (2005) ***PENDULUM - a guideline-based approach to the interpretation of STR mixtures.** *Forensic Science International* 148, 181-189.

*Curran, J.M., Gill, P. and Bill, M.R. (2005) ***Interpretation of repeat measurement DNA evidence allowing for multiple contributors and population substructure.** *Forensic Science International* 148, 47-53.

*Gill, P., Curran, J. and Elliot, K. (2005) ***A graphical simulation model of the entire DNA process associated with the analysis of short tandem repeat loci.** *Nucleic Acids Research* 33(2), 632-643.

*Jorgensen, M.A. (2005) ***Minimum message length estimation using EM methods: a case study.** *Computational Statistics & Data Analysis* (49), 147-167.

*Wright, C.L., John, J.A., Whitaker, D. and Williams, E.R. (2005) ***Construction of Resolvable Row-Column Designs based on Contractions** *Aust. N.Z. J. Stat.* (1), 119-124

*John, J.A., Russell, K.G. and Whitaker, D. (2004) ***CrossOver: an algorithm for the construction of efficient cross-over designs** *Statist. Med.* 23, 2645-2658.

*Jorgensen, M.A. (2004) ***Using multinomial mixture models to cluster internet traffic.** *Aust. N.Z. J. Stat.* 46(2), 205-218

*Newton, A.W.N., Curran, J.M., Triggs, C.M. and Buckleton, J.S. (2004) ***The consequences of potentially differing distributions of the refractive indices of glass fragments from control and recovered sources.** *Forensic Science International* 140, 185-193.

*Reed, W.J. and Jorgensen, M.A. (2004) ***The double Pareto-lognormal distribution - A new parametric model for size distributions** *Communications in Statistics: Theory and Methods* 33(8), 1733-1753.

*Bennett, R.L., Kim, N.D., Curran, J.M., Coulson, S.A. and Newton, A.W.N. (2003) ***Spatial variation of refractive index in a pane of float glass.** *Scientific and Technical* 43, 71-76.

*Curran, J.M. (2003) ***The statistical interpretation of forensic glass evidence** *International Statistical Review.* 71(3), 497-520.

*Curran, J.M., Buckleton, J.S. and Triggs, C.M. (2003) ***What is the magnitude of the subpopulation effect?** *Forensic Science International* 135, 1-8.

*Hunt, L.A. and Jorgensen, M.A. (2003) ***Mixture model clustering for mixed data with missing information.** *Computational Statistics & Data Analysis* 41, 429-440.

*John, J.A. and Russell, K.G. (2003) ***Optimising changeover designs using the average efficiency factors.** *Journal of Statistical Planning and Inference* 113, 259-268.

*Johnson, D.G. and John, J.A. (2003) ***Use of demonstrations and experiments in teaching business statistics** *Journal of Applied Mathematics and Decision Sciences* 7(2), 93-103.

*McBride, G.B., McWhirter, J.L. and Dalgety, M.H. (2003) ***Uncertainty in most probable number calculations for microbiological assays.** *Journal of AOAC International.* 86(5), 1084-1088.

*Walsh, S.J., Triggs, C.M., Curran, J.M., Cullen, J.R. and Buckleton, J.S. (2003) ***Evidence in support of self-declaration as a sampling method for the formation of sub-population DNA databases.** *Journal of Forensic Science* 48(5), 1091-1093.

*Williams, E.R. and John, J.A. (2003) ***A note on the design of unreplicated trials.** *Biometrical Journal.* 45(6), 751-757.

*Abaz , J., Walsh, S., Curran, J., Moss, D., Cullen, J., Bright, J., Crowe, G., Cockerton, S. Power, T. (2002) ***Comparison of variables affecting the recovery of DNA from common drinking containers. ** *Forensic Science International* 126(3) 233-240.

*Butcher, P.A., Williams, E.R., Whitaker, D., Ling, S., Speed, T.P. and Moran, G.F. (2002) ***Improving linkage analysis in outcrossed forest trees - an example from Acacia mangium.** *Theoretical and Applied Genetics* 104, 1185-1191.

*Curran, J.M., Buckleton, J.S., Triggs, C.M. and Weir, B.S. (2002) ***Assessing uncertainty in DNA evidence caused by sampling effects.** *Science & Justice*, 42(1), 29-37.

*John, J.A., Ruggiero, K. and Williams, E.R. (2002) ***a**_{n}
-Designs *Aust. N.Z. J.Stat.* 44(4) 457-465.

*Bolstad, W.M. and Manda, S. (2001) ***Investigating Child Mortality in Malawi using Family and Community Random Effects: A Bayesian Analysis
** *Journal of the American Statistical Association* Vol. 96.

*Bolstad, W.M., Hunt, L.A., McWhirter, J.L. (2001) ***Sex, Drugs and Rock & Roll survey in a First year service course in statistics** *The American Statistician* 55, 145-149.

*Hunt, L.A. & Basford, K.E. (2001) ***Fitting a mixture model to three-mode three-way data with missing information.** Journal of Classification, 18, 209-226.

*John, J.A. (2001) ***Updating formula in an analysis of variance model.** *Biometrika.* 88, 1175-1178.

*Buckleton, J.S., Curran, J.M, and Basten, C.J. (2000) ***Evaluating the statistical significance of single band profiles in VNTR analyses** *Science and Justice* 40(1), 27-32.

*Buckleton, J.S., Curran, J.M. and Basten, C.J. (2000) ***Evaluating the statistical significance of single band profiles in VNTR analyses.** *Science & Justice*, 40(1), 27-31.

*Curran, J.M. (2000) ***Expert Witnesses in DNA cases.** *The New Zealand Law Journal*, 101-102.

*Graham, J., Curran, J.M. and Weir, B.S. (2000) ***Conditional genotypic probabilities for microsatellite loci.** Genetics 155, 1973-1980.

*John, J.A. and Whitaker, D. (2000) ***Recursive formulae for the average efficiency factor in block and row-column
designs. ** *J. Roy. Statist. Soc., B. *62, 575-583.

*John, J.A. and Williams, E.R. (2000) ***Updating the average efficiency factor in alpha-designs** *Biometrika. *87, 695-699.

*Bardsley, W.E., Jorgensen, M.A., Alpert, P. and Ben-Gai, T. (1999) ***A significance test for empty corners in scatter diagrams** *Journal of Hydrology*, 219, 1-6.

*Curran, J.M., Buckleton, J.S. and Triggs, C.M. (1999) ***The robustness of a continuous likelihood approach to Bayesian analysis of forensic glass evidence** *Forensic Science International* 104, 93-101.

*Curran, J.M., Buckleton, J.S. and Triggs. C.M. (1999) ***Commentary on Koons RD, Buscaglia, J. The forensic significance of glass composition and refractive index measurements.** *The Journal of Forensic Sciences*, 44(4), 1324-1325.

*Curran, J.M., Robertson, B. and Vignaux, G.A. (1999) ***Genetic matches and the logic of the law** *Genetica* 105(2), 211-213.

*Curran, J.M., Triggs, C.M., Buckleton, J.S. and Coulson, S. (1999) ***Combining a continuous Bayesian approach with grouping information** *Forensic Science International* 91, 181-96.

*Curran, J.M., Triggs, C.M., Weir, B.S. and Buckleton, J.S. (1999) ***The interpretation of DNA mixtures with population structure** *Journal of Forensic Sciences* 44(5), 987-995.

*Eccleston, J. and Whitaker, D. (1999) ***On the design of optimal change-over experiments through multi objective simulated annealing ** *Statistics and Computing* 9:1, 37-42.

*Green, J.D., Shiel, R.J. & Littler, R.A. (1999) ***Boeckella major (Copepoda: Calanoida): a predator in Australian ephemeral pools.** *Archiv fur Hydrobiologie* 145, 181-196.

*Hunt, L.A. and Basford, K.E. (1999) ***Fitting a mixture model to three-mode three-way data with categorical and continuous variables** *Journal of Classification* 16, 283-296.

*Hunt, L.A. and Jorgensen, M.A. (1999) ***Mixture Model Clustering using the MULTIMIX program** Austral. & New Zealand J Statistics 41, 153-171.

*John, J.A. and Ruggiero, K. (1999) ***Resolvable block designs for factorial experiments** *J. Statist. Planning and Inference *77, 293-9.

*John, J.A. and Williams, E.R. (1999) ***Partially-latinized designs** *Statist. and Comp. *9, 203-7.

*John, J.A., Russell, K.G., Williams, E.R. and Whitaker, D. (1999) ***Resolvable designs with unequal block sizes** *Austral. & N.Z. J. Statist. *41, 111-6.

*Jorgensen, M. (1999) ***A dynamic EM algorithm for estimating mixture proprtions** *Statistics and Computing* 9(4) 299-302.

*Jorgensen, M. (1999) ***Model-Robust parameter dispersions for iteratively reweighted least squares** *Commun. Stat. Theory & Methods* 28(8) 1903-1919.

*Russell, K.G. and John, J.A. (1999) ***A note on the optimality of resolvable block designs with unequal block sizes** *J. Statist. Planning and Inference* 81, 195-9.

*Williams, E.R. and John, J.A. (1999) ***Construction of resolvable designs with nested treatment structure** *Biom. J. *41, 341-9.

*Williams, E.R., John, J.A. and Whitaker, D. (1999) ***Block designs for plant and tree breeding trials** *Austral. & N.Z. J. Statist. *41, 277-84.

*Curran, J.M., Triggs, C.M. and Buckleton, J.S. (1998) ***Sampling in forensic comparison problems** *Science and Justice* 38(2), 101-107.

*Curran, J.M., Triggs, C.M., Hicks-Champod, T., Buckleton, J.S. and Walsh, K.A.J. (1998) ***Assessing transfer probabilities in a Bayesian interpretation of forensic glass evidence** *Science and Justice* 38(1), 15-22.

*Draper, N.R. and John, J.A. (1998) ***Response surface design where levels of some factors are difficult to change** *Austral. & N.Z. J. Statist.* 40, 487-95.

*John, J.A. and Williams, E.R. (1998) ***t-Latinized designs** *Austral. & N.Z. J. Statist. *40, 111-8.

*Jorgensen, M. and Gentleman, R . (1998) ***Data Mining ** *Chance *11, 42, 34-39.

*Wood, J. and Whitaker, D. (1998) ***Student centred school timetabling** *Journal of the Operational Research Society* 49, 1146-1152.

*Curran, J.M., Triggs, C.M., Almirall, J.R, Buckleton, J.S. and Walsh, K.A.J. (1997) ***The interpretation of elemental composition measurements from forensic glass evidence** *Science and Justice* 37(4), 241-4.

*Curran, J.M., Triggs, C.M., Almirall, J.R, Buckleton, J.S. and Walsh, K.A.J. (1997) ***The interpretation of elemental composition measurements from forensic glass evidence II** *Science and Justice* 37(4), 245-9.

*John, J.A. and Williams, E.R. (1997) ***The construction of two-replicate row-column designs for use in field trials** *Appl. Statist. *46, 207-14.

*Williams, E.R. and John, J.A. (1996) ***Row-column factorial designs for use in agricultural field trials** *Appl. Statist. *45, 39-46.

*Jorgensen, M. (1994) ***Tail functions and iterative weights in binary regression** *American Statistician *48.

*Jorgensen, M. (1993) ***Influence functions for iteratively defined statistics ** *Biometrika *<80, 253-265, 1993.

*Jorgensen, M. (1990) ***Influence based diagnostics for finite mixture models ** *Biometrics* 46, 1047-1058.

*Jorgensen, M. (1989) ***Fitting nonlinear models: Keep it simple** *New Zealand Statistician *24, 36-42.

*Jorgensen, M. (1987) ***Jackknifing, fixed points of iterations** *Biometrika *74, 207-211.

*Whitaker, D. (1995) ***A nested simulated annealing algorithm** *J. Statist. Comp. Simul.*, 53, pp233-241.

*Miller, S.D., MacInnes, H.E. and Fewster, R.M. (2008) ***Detecting invisible migrants: an application of genetic methods to estimate migration rates.** In Thomson, D.L., Cooch, E.G. and Conroy M.J. (eds) *Environmental and Ecological Statistics*, Vol. 3 *Modeling Demographic Processes in Marked Populations*, pp. 417-437.

*Curran, J.M. (2005) ***Appendix 5.1 and Chapter 6 - Sampling Effects** *Forensic DNA evidence interpretation* edited by John Buckleton, Christopher M. Triggs & Simon J. Walsh. Published by CRC Press, 195-216.

*Curran, J.M. (2004) ***Using the Included R Functions** *Introduction to Bayesian Statistics* by William M. Bolstad published by Wiley, 317-327.

*Bolstad, W. M. (2010) ***Understanding Computational Bayesian Statistics** John Wiley & Sons, New York

*A hands-on introduction to computational statistics from a Bayesian point of view. Providing a solid grounding in statistics while uniquely covering the topics from a Bayesian perspective. **Understanding Computational Bayesian Statistics* successfully guides readers through this new, cutting-edge approach. With its hands-on treatment of the topic, the book shows how samples can be drawn from the posterior distribution when the formula givings its shape is all that is known, and how Bayesian inferences can be based on these samples from the posterior. These ideas are illustrated on common statistical models, including the multiple linear regression model, the hierarchical mean model, the logistic regression model and the proportional hazards model.

*Bolstad, W.M. (2007) ***Introduction to Bayesian Statistics: (Second Edition) (Wiley)**

*Bolstad, W.M. (2004) ***Introduction to Bayesian Statistics** Wiley

*John, J.A., Whitaker, D. and Johnson, D.G. (2001) ***Statistical Thinking for Managers** Chapman and Hall/CRC Press, 282p.

*Curran, J.M., Hicks, T.N. and Buckleton, J.S. (2000) ***Forensic Interpretation of Glass Evidence** CRC Press

*John, J.A. and Williams, E.R. (1995) **Cyclic and Computer Generated Designs. *Chapman and Hall, London.

*Jorgensen, M.A. (2001) ***Expectation-maximization algorithm.** Encyclopaedia of Environmetrics, Wiley, London. v2. pp 637-653.

*Jorgensen, M.A. (2001) ***Iteratively reweighted least squares.** Encyclopaedia of Environmetrics, Wiley, London. v.2, pp1084-1088.

*Jorgensen, M.A. (2001) ***Robust Regression.** Encyclopaedia of Environmetrics, Wiley, London. v.3, pp1890-1896.

*Whitaker, D. (2001) ***Branch and Bound algorithm.** Encyclopaedia of Environmetrics, Wiley: London. v.1, pp229-232.

*Whitaker, D. (2001) ***Dynamic Programming.** Encyclopaedia of Environmetrics, Wiley: London. v.1, pp578-582.

*Joshi, C., Laughlin, D.C., Van Bodegom, P.M., Bastow, Z.A., and Fule, P.Z. (2012) **Modeling trait based ecological community assembly* Proceedings of the 27th International Workshop on Statistical Modelling, Volume I, pages 177-178. Publisher: Tribune EU

*Joshi, C. (2012) ***On Computationally Efficient Estimation of the Variance of the Randomised Quasi Monte Carlo Estimate** 10th International Conference on Monte Carlo and Quasi Monte Carlo Methods in Scientific Computing (MCQMC 2012), Sydney, Australia

*Campbell, A., Hunt, L., and Bowell, T. (2011) ***Student perceptions of the value of Panopto** National Tertiary Learning and Teaching Conference, Nelson, NZ, 12-14 October

*New technologies, used wisely, allow the opportunity to offer students more flexible means of learning. For the last two years, staff at the University of Waikato have been able to use the lecture capture technology "Panopto" to record lectures for later viewing by students. However, many lecturers express concerns about the value of making learning materials available in this way. In June 2011, students in three papers (biology, philosophy, and statistics) were surveyed on whether they made use of these recorded lectures, and their perceptions of their value. The teaching staff concerned also discussed their own perceptions, and this has opened up further possibilities for research. We anticipate fruitful discussion with participants in this presentation.*

*Miller, Steven (2011) ***Coping in the Absence of Likelihoods** NZ Statistics Association Conference, Auckland NZ, 28-31 August 2011

*Miller, S.D. (2010) ***Characterising the spread of Neolithic culture across Europe** Australian Statistical Conference, Statistics in the West: Understanding our world. 6-10 December 2010 Fremantle, Western Australia

*The Neolithic era, the last major sub-division of the Stone age, began about 11,000 years ago when agriculture was developed in the Middle East. From there, Neolithic culture spread into Europe, with the era ending in Europe about 5,000 years ago, upon the widespread adoption of metal tools. The spread of Neolithic culture could have resulted from a migration of people, a diffusion of ideas, or more likely a combination of the two. Residual signals from this transition across Europe might still be found in ancient and potentially modern patterns of genetic and linguistic variation, as well as in the archaeological record. We are attempting to formally model this process by combining evidence from these multiple sources in association with a non-trivial diffusion model across a landscape. This has required the development of a method involving indirect inference to extract information from a series of computer simulations in order to estimate distributions for the diffusion parameters of interest.*

**Joshi, C.**, Brown, P.T., and Laughlin, D.C. (2013) *Inferential tests and modelling of functional trail convergence along environmental gradients* Proceedings of the World Statistics Congress 2013 (in print)

*Durrant, R.J. and Kaban, A. (2013) ***Sharp Generalization Error Bounds for Randomly-projected Classifiers** Proc. 30th International Conference on Machine Learning; JMLR W&CP 28(3):693-701

*Durrant, R.J., and Kaban, A. (2013) ***Random Projections as Regularizers: Learning a Linear Discriminant from Fewer Observations than Dimensions** *to appear*Proceedings 5th Asian Conference on Machine Learning

*Curran, J.M. (2003) ***Modelling the distribution of recovered glass.** *Proceedings of the 54th Session of the International Statistical Institute (ISI).* Berlin.

*Durrant, R.J. and Kaban, A. (2012) ***Error Bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert Space** Proc 15th International Conference on Artificial Intelligence and Statistics (AIStats 2012). JMLR W&CP 22: pp337-345.

*Fewster, R.M., Miller, S.D., and Ritchie, J. (2011) ***DNA profiling - a management tool for rat eradication**

*Bolstad, W.M. (2005) ***An Efficient Mixture-based Shrinkage Estimator: A Monte Carlo Analysis**. Edited proceedings of ASA Joint Statistical Meeting - Bayesian Section, *Minneapolis 2005 Abstract Book*

*Fewster, R.M., Miller, S.D., and Ritchie, J. (2011) **DNA profiling - a management tool for rat eradication* Island Invasives: Eradication and Management (eds C.R. Veitch, M.M. Clout, and D.R. Towns), Proceedings of the International conference on Island Invasives, 426-431.

*McWhirter, J.L. (2004) ***Lotto prices up, chances down by Ainsley Thomson** NZ Herald 27.7.04

*John, J.A. (2002) ***Crossover Designs in Clinical Trials.** 16th Australian Statistical Conference, Canberra.

*Hunt, L.A. & Jorgensen, M.A. (2001) ***Clustering via Mixture Models.** Mixtures 2001 Conference, Hamburg, Germany

*Laughlin, D.C., Joshi, C., Van Bodegom, P.M., Bastow, Z.A. and Fule, P.Z. (2012) **A predictive model of community assembly that incorporates intraspecific trail variation.* Blackwell Publishing Ltd; pages 1291-1299 (November 2012)

*Jung, Y., MacEachern, S.N., and Lee, Y. (2010) **Window Width Selection for L2 Adjusted Quantile Regression* Technical Report No 835, Department of Statistics, The Ohio State University.

*Calude, A.S. and Miller, S. (2009) ***Are clefts contagious in conversation?** English Language and Linguistics (13), pp. 127-132

*Buckleton, J.S., Triggs, C.M and Curran, J.M. (2001) ***Detection of deviations from genetic equilibrium - a commentary on Budowle B et al. Population data on the thirteen CODIS core short tandem repeat loci in African Americans, US Caucasians, Hispanics, Bahamians, Jamaicans, and Trinidadians.** *J. Forensic Science.* 46(1), 198-200.

*Bolstad, W.M. (2007) ***Bayesian analysis using Minitab macros** www.minitab.com/support/macros/macrolinks.aspx

*Dale, J. (1996) ***Statistics: Servant of technology ** In *New Zealand Statistical Association 47th Annual Conference Proceedings* Wellington, 8-10.

*Roddy, B.P., McWhirter, J.L., Moon, V.G., Balks, M.R., Dyer, F.J. (2008) ***Sediment fingerprinting in New Zealand: a pilot study into the feasibility of the application of the technique** Proceedings of a symposium held in Christchurch, New Zealand, December 2008. IAHS Publ. 325, 2008, 143-146.

*Statistically Verified Composite Fingerprinting (SVCF) has been attracting increased attention to identify catchment erosion sources, but to date has not been applied in a New Zealand setting. A pilot study was undertaken to investigate if different catchment land-uses (native forest, exotic forest and pastoral agriculture) can be discriminated by their geochemical fingerprints. Seven elements were found to discriminate between the three land-use types, and a novel method of data resampling was successfully used to verify the selection of fingerprint properties.
www.iahs.info
*

*Bolstad, W.M. (2002) ***Teaching Bayesian Statistics to Undergraduates: Who, What, Where, When, Why and How.** *International Conference on Teaching Statistics (ICOTS6)*, Cape Town.

*John, J.A. and Johnson, D.G. (2002) ***Statistical Thinking for Effective Management** *International Conference on the Teaching of Statistics (ICOTS6)* Cape Town, South Africa,

*Jorgensen, M.A. (2001) ***Clustering via Mixture Models: some issues.** Proceedings of the 10th International Symposium on Applied Stochastic Models and Data Analysis, Compiegne. 2, 585-590.

*Jorgensen, M.A. (2001) ***Method Comparison via Single Factor Analysis.** Proceedings of the 16th International Workshop on Statistical Modelling, Odense. 243-250.

*Jorgensen, M.A. and Hunt, L.A. (1996) ***Mixture Model Clustering of Data Sets with Categorical and Continuous Variables** *Proceedings of the Conference, ISIS '96, Australia*, p375-384.

*Jorgensen, M.A. and Hunt, L.A. (1994) ***Mixture model clustering of data sets with categorical and continuous variables** *Proceedings of the Conference NZSA/ORSNZ*

*Curran, J.M. (2002) ***Improvements in and relating to interpreting DNA**

*Jorgensen, M.A. and Hunt, L.A. (1995) ***Mixture Model Clustering of Data Sets with Categorical and Continuous Variables** Waikato University Research Report No. 36 Series II.

*Bolstad, W.M. (2007) ***Bayesian Process Monitoring Control and Optimization by Bianca M. Colosimo & Enrique del Castillo**

*Bolstad, W.M. (2005) ***Bayesian Nonparametrics via Neural Networks** *by Herbert H.K. Lee.* (SIAM, Philadephia, 2004.) SIAM REVIEW Vol.47 No.4 pp 810-812

*Bolstad, W.M. (2005) ***Most Honourable Remembrance: The Life and Work of Thomas Bayes***by Andrew Dale.* (Springer-Verlag, New York, 2003). SIAM REVIEW Vol.47 No.2 pp 369-410