Keynote address. Young Statisticians Conference 2013.

For 25 years I have been an intrepid statistical consultant, tackling the wild frontiers of real data, real problems and real time constraints. I have faced problems ranging from linguistics to river beds, from making paper plates to selling pies at the MCG, from tax office audits to surveys about the colour purple. University education helps prepare you to be a statistical consultant in the same way that Google maps helps prepare you to cross the Simpson Desert. You have some idea of the main features, but when you get there, nothing looks familiar.

I will describe some of my adventures, and explain how to bluff your way through ignorance, work with completely inadequate tools, and deal with smelly clients. I will tell you the story of the client who wouldn’t give me the data, the client who wouldn’t tell me the problem, and the client who wanted all meetings held at random locations for security reasons.

Along the way we will learn about the skills that statisticians need to survive in the wild.


1 2 3 5
November 26th, 2015

Forecasting hierarchical and grouped time series through trace minimization

November 18th, 2015

Fast computation of reconciled forecasts for hierarchical and grouped time series

November 2nd, 2015

Forecasting big time series data using R

October 7th, 2015

Optimal forecast reconciliation for big time series data

October 5th, 2015

Google workshop: Forecasting and visualizing big time series data

September 25th, 2015

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

September 25th, 2015

On sampling methods for costly multi-objective black-box optimization

September 16th, 2015


August 29th, 2015

Forecasting with temporal hierarchies

August 25th, 2015

New IJF editors

August 17th, 2015

Machine learning bootcamp

August 7th, 2015

Statistical issues with using herbarium data for the estimation of invasion lag-phases

July 22nd, 2015

Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation

June 30th, 2015

Exploring the feature space of large collections of time series

June 26th, 2015

Exploring the boundaries of predictability: what can we forecast, and when should we give up?

June 25th, 2015

Automatic algorithms for time series forecasting

June 23rd, 2015

MEFM: An R package for long-term probabilistic forecasting of electricity demand

June 19th, 2015

Probabilistic forecasting of peak electricity demand

June 10th, 2015

Do human rhinovirus infections and food allergy modify grass pollen–induced asthma hospital admissions in children?

June 8th, 2015

STR: A Seasonal-Trend Decomposition Procedure Based on Regression

June 4th, 2015

Probabilistic time series forecasting with boosted additive models: an application to smart meter data

June 1st, 2015

Large-scale unusual time series detection

May 26th, 2015

Visualization of big time series data

May 22nd, 2015

Probabilistic forecasting of long-term peak electricity demand

April 20th, 2015

A note on the validity of cross-validation for evaluating time series prediction

April 4th, 2015

Discussion of “High-dimensional autocovariance matrices and optimal linear prediction”

April 1st, 2015

Change to the IJF editors

February 23rd, 2015

Visualization and forecasting of big time series data

January 12th, 2015

Visualizing and forecasting big time series data

December 24th, 2014

Bivariate data with ridges: two-dimensional smoothing of mortality rates

December 17th, 2014

MEFM package for R

October 21st, 2014

Optimally reconciling forecasts in a hierarchy

September 23rd, 2014

Forecasting: principles and practice (UWA course)

September 1st, 2014

Outdoor fungal spores are associated with child asthma hospitalisations – a case-crossover study

August 1st, 2014

Efficient identification of the Pareto optimal set

July 1st, 2014

Fast computation of reconciled forecasts in hierarchical and grouped time series

June 24th, 2014

Functional time series with applications in demography

June 17th, 2014

Challenges in forecasting peak electricity demand

June 5th, 2014

Low-dimensional decomposition, smoothing and forecasting of sparse functional data

May 30th, 2014

State space models

May 24th, 2014

Common functional principal component models for mortality forecasting

May 22nd, 2014

Monash Electricity Forecasting Model

May 8th, 2014

forecast package for R

April 9th, 2014

hts package for R

April 1st, 2014

A gradient boosting approach to the Kaggle load forecasting competition

March 31st, 2014

Measuring forecast accuracy

February 13th, 2014

Automatic time series forecasting

February 1st, 2014

demography: Forecasting mortality, fertility, migration and population data

January 10th, 2014

Boosting multi-step autoregressive forecasts

January 1st, 2014

Forecasting: principles and practice