Pearson's chi-squared, the likelihood-ratio, and Fisher-Freeman-Halton's test statistics are often used to test the association of unordered r x c tables. Asymptotical, exact conditional, or exact conditional with mid-p adjustment methods are commonly used to compute the p-value. We have compared test power and significance level for these test statistics and p-value calculations in small sampl...

Kolmogorov in 1965 proposed two related measures of information content (alternately, measures of complexity) based on the size of a program which when processed by a suitable algorithm (machine) yields the desired object. The main emphasis was placed on a conditional complexity measure. In this paper a simple variation of the (restricted) conditional complexity measure investigated by Martin-L...

Multi-stage additive tests are commonly used in applications. Appropriate definition of its test decisions, however, turns out to be challenging. There are a number of existing methods for this purpose, mainly by combining p-values of individual tests using a conditional error probability function. While these methods are flexible enough to use in most applications, their results depend on the ...

As applied problems have grown more complex, statisticians have been gradually led to reconsider the foundations of statistical inference. The recently proposed inferential model (IM) framework of Martin, Zhang and Liu (2010) achieves an interesting compromise between the Bayesian and frequentist ideals. Indeed, inference is based on posterior probability-like quantities, but there are no prior...

Exchangeable models for vertex labeled graphs cannot replicate the large sample behaviors of sparsity and power law degree distributions observed in many network datasets. Out of this mathematical impossibility emerges the question of how network data can be modeled in a way that reflects known empirical behaviors and respects basic statistical principles. We address this question by observing ...

Conditional knowledge bases have been proposed as belief bases that include defeasible rules (also called defaults) of the form “ ! ”, which informally read as “generally, if then .” Such rules may have exceptions, which can be handled in different ways. A number of entailment semantics for conditional knowledge bases have been proposed in the literature. However, while the semantic properties ...

The paper examines the extent to which the conditional volatility of stock market returns in a small, internationally integrated stock market are related to the conditional volatility of financial and business cycle variables. It employs a low frequency monthly dataset for Australia including stock market returns, interest rates, inflation, the money supply, industrial production and the curren...

In this paper, we present the experiments we made to process entities from the biomedical domain. Depending on the task to process, we used two distinct supervised machine-learning techniques: Conditional Random Fields to perform both named entity identification and classification, and Maximum Entropy to classify given entities. Machine-learning approaches outperformed knowledge-based technique...

Given the likely large impact of artificial general intelligence, a formal theory of intelligence is desirable. To further this research program, we present a representation theorem governing the integration of causal models with decision theory. This theorem puts formal bounds on the applicability of the submodel hypothesis, a normative theory of decision counterfactuals that has previously be...