In the 2020/21 academic year, I will be
available to handle one 548 paper in each of October, November, Feb and March. The assessment criteria for Stat 548 papers
are posted on the web, but in general I am hoping to see well written reports
that explain results in the papers, how they were found, additional examples, gaps and shortcomings in the work, as well as
interesting proposals for future work.

The papers:

· Diggle, P.J., Menezes, R. and Su,
T-L. (2010). Geostatistical inference under preferential
sampling. Appl. Statist. 59, 191-232. link This paper, a
landmark in spatial statistics, describes/assesses the potential effect of selecting sites in biased way for a network designed to monitor an environmental/spatial process, An example is a set of urban ozone monitors set up specifically to
find detect noncompliance with regulatory standard so as to protect human
health. Ironically the data from such a monitoring network will unestimate the
effects of ozone. The paper, which is now something of a classic, sparked
interest in a topic that has turned into a very active research area in
environmetrics. PAPER HAS BEEN TAKEN FOR 2020/2021

· Shen, W., Davis, T., Lin, D.K.J. and Nachtsheim, C.J.(2014). Dimensional analysis and its
applications in statistics. J of Quality Technology, 46,
185-198. link This paper
concerns an important topic in the application of statistics, namely, the dimensions, scales and units of measurement on which the data
were collected. No doubt this is in part because data in datasets don't come with units
attached. But perhaps its also due to the training statisticians receive that is
heavily based on mathematical and computational formalities. As a result a lot
of the tables and graphs one sees in publications are meaningless since the units
are not attached. But a more fundamental issue, the one addressed in this paper
has to do with the fact that not all models are valid due to their failure to
recognize these dimensions. Moreover in many cases, recognizing those
limitations on models imposed by the dimensions actually simplifies the
process of modelling the data. This paper is a well-recognized contribution
to the literature on that topic. It would be great if you could come up with
an additional example!

· Wakefield, J and Shaddick, G.S (2006). Health-exposure
modeling and the ecological fallacy. Biostatistics, 7,
438-455. link This paper
concerns an important issue in the application of statistics, notably in health related research, for example in epidemiology. An issue arises
when data are aggregated as they must be sometimes when official statistics
such as when the number of deaths are reported. In that example administrative
records may give those numbers for districts while the levels of a hazardous
substance such as air pollution levels are given for a few specific locations in
each such district. The ecological fallacy may then arise: the association between
the two sorts of data may be negative at the aggregate level and positive when
smaller subregions are analysed using the same data. Hence this important
phenomenon has been much studied. This paper is a well-recognized contribution
to the literature of that subject.

·
Evans, J.W., Johnson, R.A. and Green, D.W. (1984).
Estimating the correlation between variables under destructive testing, or how
to break the same board twice.
Technometrics., 26, 285-290. link

This paper illustrates the magic of statistics. It
concerns a problem that arises in structural engineering where the strength of structural
members such as a piece of lumber play an important role in determining the
strength of a structure, e.g. the LSK building at UBC! One such measure is the failure
load that breaks the member, when stretched. Another is its bending
strength. However, you cannot break the
same member twice to determine the relationship between these two measures of
strength, thereby potentially eliminating the need to measure both as one can
be predicted from the other (as in the case of carbon fibre panels for AirBus
aeroplane wings). The problem is that you cannot break the same
specimen twice. Or can you? This paper
shows the answer can be a yes.

·
McClintock, B.T., Johnson, D.S. Hooten, M.B., Ver
Hoef, J.M. and Morales, J.M. (2014).
When to be discrete: the importance of time formulation in understanding animal
movement. Movement Ecology, 1-14. link

The explosion in new technology we are seeing today, has
led to tags for tracking animal movement, so small that even small birds can be
tracked with the goal of determining where they go and ultimately perhaps, why
they go there. However the resulting data records can be of huge dimension, well
beyond the scope of conventional software for analysis. For example, a single female
seal foraging for food to feed her pups, can go out to sea for 10 to 20 days
before returning to land to feed her pubs, thereby creating a data record, 700,000 items long. This paper by a
distinguished team of statistical ecologists and statisticians, discusses the
problem of modelling animal tracks and when you can assume the time domain is
discrete as against continuous. The
subject involves stochastic processes, e.g. Browian motion.

· Michela Cameletti, Finn Lindgren, Daniel Simpson, Havard Rue
(2012). Spatio-temporal modeling of particulate matter
concentration through the SPDE approach. AStA Adv Stat
Anal. To appear. link

This
paper describes a valuable new approach to modelling random spatial fields over
large domains, for example, temperature over the earth's surface, where the
dimension of the multivariate response vector, each coordinate representing a
geographical site, is so large that traditional methods for Bayesian analysis
such as MCMC cannot possibly be used. The method, INLA
is developed using a link between a stochastic partial differential equation
and the famous Matern covariance model. The application concerns a nasty air
pollution field formed from small particles that are strongly linked to adverse
health effects, like mortality due to cardiovascular problems. Computing and
math background will be desirable, but the paper seems pretty readable and
self-contained.

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