  Model order selection (MOS) in linear regression models is a widely studied
problem in signal processing. Techniques based on information theoretic
criteria (ITC) are algorithms of choice in MOS problems. This article proposes
a novel technique called residual ratio thresholding for MOS in linear
regression models which is fundamentally different from the ITC based MOS
criteria widely discussed in literature. This article also provides a rigorous
mathematical analysis of the high signal to noise ratio (SNR) and large sample
size behaviour of RRT. RRT is numerically shown to deliver a highly competitive
performance when compared to popular model order selection criteria like Akaike
information criterion (AIC), Bayesian information criterion (BIC), penalised
adaptive likelihood (PAL) etc. especially when the sample size is small.
